Health impact assessment of waste management facilities in three European countries

  • Francesco Forastiere1Email author,

    Affiliated with

    • Chiara Badaloni1,

      Affiliated with

      • Kees de Hoogh2,

        Affiliated with

        • Martin K von Kraus3,

          Affiliated with

          • Marco Martuzzi4,

            Affiliated with

            • Francesco Mitis4,

              Affiliated with

              • Lubica Palkovicova5,

                Affiliated with

                • Daniela Porta1,

                  Affiliated with

                  • Philipp Preiss6,

                    Affiliated with

                    • Andrea Ranzi7,

                      Affiliated with

                      • Carlo A Perucci1 and

                        Affiliated with

                        • David Briggs2

                          Affiliated with

                          Environmental Health201110:53

                          DOI: 10.1186/1476-069X-10-53

                          Received: 10 November 2010

                          Accepted: 2 June 2011

                          Published: 2 June 2011

                          Abstract

                          Background

                          Policies on waste disposal in Europe are heterogeneous and rapidly changing, with potential health implications that are largely unknown. We conducted a health impact assessment of landfilling and incineration in three European countries: Italy, Slovakia and England.

                          Methods

                          A total of 49 (Italy), 2 (Slovakia), and 11 (England) incinerators were operating in 2001 while for landfills the figures were 619, 121 and 232, respectively. The study population consisted of residents living within 3 km of an incinerator and 2 km of a landfill. Excess risk estimates from epidemiological studies were used, combined with air pollution dispersion modelling for particulate matter (PM10) and nitrogen dioxide (NO2). For incinerators, we estimated attributable cancer incidence and years of life lost (YoLL), while for landfills we estimated attributable cases of congenital anomalies and low birth weight infants.

                          Results

                          About 1,000,000, 16,000, and 1,200,000 subjects lived close to incinerators in Italy, Slovakia and England, respectively. The additional contribution to NO2 levels within a 3 km radius was 0.23, 0.15, and 0.14 μg/m3, respectively. Lower values were found for PM10. Assuming that the incinerators continue to operate until 2020, we are moderately confident that the annual number of cancer cases due to exposure in 2001-2020 will reach 11, 0, and 7 in 2020 and then decline to 0 in the three countries in 2050. We are moderately confident that by 2050, the attributable impact on the 2001 cohort of residents will be 3,621 (Italy), 37 (Slovakia) and 3,966 (England) YoLL. The total exposed population to landfills was 1,350,000, 329,000, and 1,425,000 subjects, respectively. We are moderately confident that the annual additional cases of congenital anomalies up to 2030 will be approximately 2, 2, and 3 whereas there will be 42, 13, and 59 additional low-birth weight newborns, respectively.

                          Conclusions

                          The current health impacts of landfilling and incineration can be characterized as moderate when compared to other sources of environmental pollution, e.g. traffic or industrial emissions, that have an impact on public health. There are several uncertainties and critical assumptions in the assessment model, but it provides insight into the relative health impact attributable to waste management.

                          Background

                          Waste collection, transport, processing, and disposal are important for both environmental and public health reasons. Different strategies have been proposed to reduce, reuse, recycle, recover energy and finally dispose of municipal solid waste (MSW) [1, 2] but the environmental impacts of these strategies are controversial [3]. Several studies of the possible health effects on populations living in proximity of landfills and incinerators have been published [4] and both landfills and incinerators have been associated with some reproductive and cancer outcomes. However, the results of these studies are either inconclusive or contradictory. Consequently, there is controversy over the possible health implications of waste management policies [5] and both policy makers and the public require more information on the likely health impacts (and importantly, the associated nature and extent of the uncertainties).

                          Within the EU-funded INTARESE project [6], we aimed at assessing potential exposures and health effects arising from municipal solid wastes, from generation to disposal, or treatment (see Figure 1 to appreciate the full chain of the waste problem). The background of the project is that policy cannot be served solely by using traditional risk assessment methods. Instead, the assessment needs an approach that recognises the complexity of the systems involved, and integrates the different information, knowledge, analytical methods and tools needed to approach the way these complex systems behave. It was this need that motivated the term "integrated environmental health impact assessment" (IEHIA), whose rationale, guidance and instruments are illustrated in the toolbox available on the web (http://​www.​integrated-assessment.​eu).
                          http://static-content.springer.com/image/art%3A10.1186%2F1476-069X-10-53/MediaObjects/12940_2010_446_Fig1_HTML.jpg
                          Figure 1

                          The full chain approach - from waste production to health effects.

                          We performed a diagnostic assessment for three EU countries (Italy, Slovakia and England) considering the baseline situation in 2001 in order to assess the health burden or risk attributable to specified factors. This initial approach might be of interest for ranking different risks to health in terms of their overall burden of disease, and thus for prioritising policy action. For reasons of feasibility in our study, not all the aspects illustrated have been considered here (e.g. waste transport, occupational factors, greenhouse gases, risk perception etc) and they could be part of a more complete and exhaustive exercise. However, we aimed to establish a baseline scenario that can be useful in the future for prognostic assessment of different policy options.

                          Each step in the exercise has several uncertainties that should be considered in the overall evaluation. For this reason, we have tried to systematically state the level of confidence we had using the scale of calibrated terminology used by the IPCC [7]: very high (at least 9 out of 10), high (about 8 out of 10), moderate (about 5 out of 10), low (about 2 out of 10), and very low (less than 1 out of 10). Such calibrated levels of confidence can be used to characterize uncertainty based on expert judgment of the correctness of a model, an analysis or a statement. The approach was also applied in the systematic review we conducted on the topic [4].

                          Methods

                          The assessment followed the steps involved in the "full chain" approach, illustrated in Figure 1.

                          Waste Generation and Management

                          Current waste management data were collected from country-specific environmental agencies. The Italian Institute for Environmental Protection and Research (ISPRA) [8] provided a database of the incinerators operating during the period 2001-2007. In addition, a detailed census of the 52 incinerators operating in 2005 was made available by a national research institute [9]. Detailed data were also provided by the regional environmental authority of Emilia Romagna for all eight incinerators located in that region. From all these sources, we were able to identify the 40 incineration plants operating in 2001, obtain their geographical coordinates and get specific information on years of operation, number of lines, fumes capacity (Nmc/h), stack height (m), stack diameter (m), exit velocity (m/s), emission rate (m3/s), and exit temperature (°C). In a few cases, when the information on technical characteristics was missing, it was approximated using information from other plants with similar characteristics.

                          The ISPRA provided a database of landfills in Italy (a total of 619 in 2001) with information of the total capacity and wasteland filled per year. Geographical coordinates of the landfills were available for only five (of twenty) regions (Piedmont and Emilia Romagna (North), Tuscany and Abruzzi (Centre), and Campania (South)) for a total of 118 landfills. For the rest of the country, we assumed that the characteristics (sex, age and socioeconomic status) of people around the 501 missing landfills were similar to those of the entire sample of the 118 sites studied.

                          For Slovakia, information on the number of incinerators handling municipal waste in 2001 was obtained from the Slovak Environmental Agency (SEA). There were two incinerators for MSW in 2001, geo-coded and with detailed information on technical characteristics, obtained from managing companies. At the end of 2001, there were 165 active landfills for municipal wastes. The list of landfills, by region, was available from the website of the Slovak Ministry of Environment [10], with geographical coordinates, capacity and starting year obtained from the SEA. Out of 165 active landfills in 2001, 121 were geocoded. We assumed that the characteristics (sex, age and socioeconomic status) of people around the 45 missing landfills were similar to those of the 121 studied sites.

                          For England, we evaluated all 11 municipal waste incinerators operating during 2001. Data on emissions of toxic substances (Tonnes/annum) and location (x- and y-coordinates) were obtained from the Environment Agency (EA). Wherever possible, more specific data (e.g. stack height, stack diameter, emission rate, etc) were obtained from the waste companies' websites. Where no data could be found, we applied an average from the known incinerator data.

                          Data for all regulated landfill sites in England and Wales was obtained from the EA. No data for 2001 was available because of changes in the regulations. In 2001 landfill sites came under a different directive and were not required to report to the EA under the Pollution Prevention and Control (PPC) Regulations. The EA advised using the 2006 landfill data instead as a good indicator for the 2001 situation [11]. The 2006 dataset contains information about 242 regulated landfill sites (e.g. geographic Cartesian coordinates, atmospheric releases).

                          Population data by gender, age and socioeconomic status

                          Population data at the smallest unit of aggregation for the 2001 census were available for the census blocks in Italy (about 100-200 (mean 162, sd 223) inhabitants per unit) and Slovakia (about 700-800 (mean 785, sd 1318) inhabitants). For England, census population data were disaggregated to postcode areas (mean 41, sd 37). For each census block in Italy, a deprivation index was available [12]. It used census information that represents various aspects of deprivation: education, occupation, home ownership, family composition and nationality. An algebraic combination of these factors was used to create an index of socioeconomic position by census block, with the corresponding population distributed in quintiles, ranging from very well off (level 1) to very underprivileged (level 5). For Slovakia, an index of socioeconomic position was derived from the following census variables: education (proportion of population with university, secondary, basic or no education), proportion of families with children, proportion of employed among 16-64 year olds, house type (house or flat), and house ownership. Again an index per census block was distributed in quintiles, ranging from very well off (level 1) to very underprivileged (level 5). For England, the Carstairs score [13], which is based on four census variables (lack of car ownership, unemployed head of the household, low social class and overcrowding) was applied as the deprivation index. The Carstairs score was available at the smallest census area, the output areas (OAs). By means of a point-in-polygon analysis the Carstairs score was transferred from the OA to each postcode. As in Italy and Slovakia, the Carstairs score in England was divided into 5 quintiles, 1 being the most affluent to 5 being the most deprived.

                          We used the distance from the point source (landfill site and/or incinerator) to estimate the exposed population. We decided to use the 3 km surrounding incinerators [14] and 2 km surrounding landfill sites [15] as the likely limit of the dispersion of emissions. For both incinerators and landfills, we defined increasing radial distances (1, 2, and 3 km) from the centre (the formal address of the plant) and evaluated the census blocks (or the postcode districts) that matched these locations. In several cases, the distribution of census blocks did not precisely fit the circle and the borders were tailored in order to more precisely count the population. The validity of the method has been evaluated using individually geocoded data of the resident population in four areas of Emilia Romagna (Italy) and considering the border of the plant area rather than the formal address: the error range was between 1 and 10%.

                          Local air dispersion modelling

                          Local air dispersion modelling was used to calculate increased pollutant concentrations (particulate matter, PM10, and Nitrogen dioxide, NO2) within 3 km of the waste incinerators. Dispersion modelling for incinerators was based on the national information on incineration census, actual waste throughput data and meteorological data. We have used the Atmospheric Dispersion Modelling System (ADMS-Urban) [16] for modelling dispersion at the local scale for 40 incinerators in Italy, 2 in Slovakia and 11 in England. Meteorological data requirements include temperature (°C), wind speed (m/s), wind direction (°), precipitation (mm), cloud cover (oktas), relative humidity (%), boundary layer height (m), and surface sensible heat flux (W/m2). We have used official meteorological data available from the nearest meteorological station. Usually 2001 meteorological data were used.

                          For PM10 and NO2 we have used emission rates based on national limits derived from EU legislation in 2001, namely daily emission rates of 10 and 200 mg/Nm3, respectively. However, since actual emissions could be estimated from Italy and England, we conducted an additional analysis using this measurement data [17, 18].

                          A number of incinerators in both Italy and England were located on hilly terrain. ADMS-Urban contains a hill module that takes into account the surrounding terrain when modelling the dispersion. Terrain data was therefore obtained for both these countries. For England the Ordnance Survey PANORAMA ™ Digital Terrain Model was used to obtain surface heights for 50 × 50 m cells up to 10 km away from 8 of the 17 incinerators. For Italy the terrain data was collected from the Italian Environmental Protection Agency for 35 of the 40 incinerators.

                          ADMS air pollution dispersion model provided "contours" of additional concentrations of PM10 and NO2 for the incinerators. These output files (one per country) have been transferred into the GIS system. The population database at the smallest available unit (i.e. census block or postcode district) for the given radius of 3 km has been added to the GIS as another data layer. Using an overlay function in GIS, the population data was combined with the air pollution concentration data with a grid of 200 meters. In this way, different statistics regarding population-weighted exposure levels have been estimated according to gender, age and socioeconomic status.

                          Exposure-response relationships

                          Following a systematic review of the literature [4], we chose to use the excess risk values reported by Elliott et al. [14] of cancer for incinerators, and of congenital malformations and low birth weights [15] for landfills. Cancer incidence between 1974 and 1987 among over 14 million people living near 72 solid waste incinerator plants in Great Britain were studied [14]. The excess risk estimate for living within 3 km of an incinerator for all cancers combined was 3.5% (95%CI = 3-4%). However, Elliott et al. point out that there was an indication of residual confounding from socioeconomic status and a concern of misdiagnosis among registrations and death certificates for liver cancer. These aspects lowered our overall confidence in the results and we rated the level of confidence of the risk estimates for cancer as "moderate".

                          In the national study conducted by Elliott et al. [15] on 9,565 landfill sites in Great Britain, operational at some time between 1982 and 1997, statistically significant increased risks were found for all congenital malformations, neural tube defects, abdominal wall defects, surgical correction of gastroschisis and exomphalos, and low and very low birth weight in residents within 2 km of the sites. Although several alternative explanations, including ascertainment bias, and residual confounding could not be excluded in the study, it provides quantitative effect estimates: the relative risk for congenital anomalies was 1.02 (99% CI = 1.01-1.03) and 1.06 (99% CI = 1.052-1.062) for low birth weight. Again, on the basis of the systematic review [4] our level of confidence in these relative risks was "moderate".

                          Linear and no-threshold exposure-response functions related to the long-term effects on mortality from PM10 and NO2 have been derived from the extensive existing reviews of epidemiological and toxicological data [19]. We assumed a linear relationship between the air pollutants and associated health effects as most epidemiological studies on large populations have been unable to identify a threshold concentration below which ambient air pollutants has no effect on morbidity and mortality. The following values were used:
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                          Background health statistics

                          Background gender-age specific cancer incidence data for the three countries were retrieved [2022] together with national mortality statistics [2224]. Prevalence of congenital malformations at birth was derived from the Annual Report (data for 2000) of the International Clearinghouse for birth defects monitoring system [25] for Italy and England, and from The Statistical Office of the Slovak Republic [24] for Slovakia.

                          Time frame of the assessment

                          Health impacts were estimated for the period 2001-2050, assuming that the incinerators operating in 2001 will be operating until 2020 - a realistic assumption given that these plants are usually in operation for a long time. The choice of 2050 ensures that the time period under consideration is long enough to account for chronic effects. For incinerators, cancer incidence "attributable" to exposure before 2001 ("past exposure") was estimated (burden of disease non-modifiable by policy) as it is likely that it will continue to appear until 2050. In addition, cancer incidence "attributable" to exposure during 2001-2020 was estimated ("current exposure") as these effects could be, at least in part, prevented. In addition, Years of Life Lost (YoLL) were estimated as attributable to current exposure (2001-2020) to PM10 and NO2 in the cohort of 2001 residents followed up to 2050.

                          For landfills operating in 2001, we assumed that the emissions will last up to 2030 (an assumption supported by the available knowledge that landfilled biodegradable waste starts to emit biogas a few years after deposit and continues to do so for several decades) and the health effects, in terms of congenital anomalies and low birth weight, are constant throughout this period. It was assumed that there would be no improvement in the technology of gas recovery. Of course, the same could be applied to contamination of groundwater and soil.

                          Estimating cancer incidence near incinerators

                          The basic formula to compute the number of cancer cases attributable to an incinerator is:
                          http://static-content.springer.com/image/art%3A10.1186%2F1476-069X-10-53/MediaObjects/12940_2010_446_Equc_HTML.gif

                          where AC = the attributable cancer incidence

                          Rate unex = background incidence rate in the general population

                          ER = excess risk in the exposed population (relative risk - 1)

                          Pop exp = number of exposed people

                          We assumed that the excess risk is not constant over time, but varies for a specific individual of the population at a given age and specific time as a function of various characteristics: level of cumulative exposure, latency since first exposure and latency since cessation of exposure (if any). We therefore assumed a theoretical model of cancer occurrence and imputed the varying excess risk around different incinerators, as a function of the different characteristics of the plant and of the nearby population. The methods are fully described in the additional file 1 (Appendix 1). Briefly, we modified the excess risk for overall cancer incidence estimated by Elliott et al. [14] (i.e. 3.5% for people exposed at incinerators operating before 1980, assuming 20 years of exposure) as a function of cumulative exposure (with exposure coefficients varying with time), latency since first exposure and latency since cessation of exposure. This algorithm was applied to the estimated 2001 population (by gender and age) living within 3 km of each specific incinerator to estimate the number of excess cancer cases in 2001-2050 attributable to exposures before 2001 and during 2001-2020. In Appendix 1, we illustrate the basic assumptions and we show how the excess risk during the 2001-2050 evaluation period varies in relation to time since the start of the operation of the plant and the time since cessation. The key assumption we made is that after 1980, due to technological improvements and as a result of national and European laws, the emissions from incinerators were reduced. For instance, measured particulate matter emissions from one incinerator in Italy (Modena) were 0.19 g/s in 1980-1989 (two lines), 0.0347 and 0.0376 g/s in 1995-1996 (two lines), 0.0196, 0.0273 and 0.104 g/s (three lines) in 1997-2002, and 0.0081, 0.0101, and 0.013 g/s (three lines) in 2003-2006. On the other hand, emission limits in the UK were reduced through legislation from 460 mg/m3 (1968) to 200 mg/m3 (1983) to 30 mg/m3 (1989/1990) and finally to 10 mg/m3 in 2000. Based on these data, we assumed that if the exposure level was 1 before 1980, it was 0.8 in 1980-1989, 0.2 in 1990-2000, and 0.05 after 2000. In other words, we are assuming that the exposure levels during the eighties were somewhat lower (0.8) than during the seventies, during the nineties were fourfold lower, and in more recently they were twentyfold lower than in the seventies. We are highly confident about the scores we gave the exposure levels, as they are confirmed from measured data and are reflected in the legislation. Overall, we have a moderate level of confidence in the estimates of cancer cases, mainly due to the uncertainty characterizing the excess risk used.

                          Estimating years of life lost (YoLL)

                          Assuming that chronic effects may continue to manifest themselves until 2050 in the entire population living close to incinerators in the three countries in 2001, and that their mortality rate was similar to that of the national population in 2001, we estimated Years of Life Lost attributable to PM10 and NO2 exposure as derived from the air dispersion model. In particular, we assumed that the impact of PM10 and NO2 was that which occurs during 2001-2020. As was done by Miller [26], we assumed constant future birth rates, constant hazard rates over time, immediate mortality effects after change in population-weighted exposure (no lag). The effects are calculated up to the year 2050 and all over a whole life span (105 years).

                          Overall, we have a high level of confidence in the estimates of YoLL, mainly due to the rather stable and well-established coefficients for NO2 and PM10.

                          Estimating congenital anomalies and low birth weight near landfills

                          With a moderate level of confidence, we assumed that the only health impacts on populations living near landfills are congenital malformations and low birth weight. Other possible health effects were not considered, as our review of the literature did not reveal any other significant health effects. As already indicated, the time of the evaluation was 2001-2030 on the assumption that gas emissions (methane, carbon dioxide, non-methane volatile organic compounds, hydrogen sulfide and ammonia) from landfills last several years even after a landfill closure (we assumed 30 years). We cannot exclude that the effect on reproductive health occurs via groundwater or soil contamination. The formula to calculate the cases of malformation and babies of low birth weight attributable to residence near landfills is the same as for cancer incidence, where incidence should be changed with prevalence at birth and the number exposed are newborns.

                          Overall, we have a moderate level of confidence in the estimates of attributable cases of congenital anomalies and low birth weight, mainly due to the uncertainty in characterizing the excess risk ratios.

                          Results

                          Waste generation and management

                          Table 1 illustrates the basic statistics of waste management in Italy, Slovakia and England in 2001. The amount of MSW produced in Italy in 2001 was 31.94 million tonnes (Mtonnes), which corresponds to 560 kilograms per inhabitant. About 56% of Italian MSW was directed to landfills; recycling and composting accounted for 16% and 8% of MSW. For Slovakia, data on MSW production and management were available for 2002. The amount of MSW produced in that year was 1.52 million tonnes, which corresponds to 283 kilograms per inhabitant. From this total, 12% (0.18 Mtonnes) was recovered/treated (2.4% recycled, 2.6% composted, 6% recovered as energy and 1% treated by other methods) and 82% was disposed of (4.3% incinerated, 78.2% landfilled and 5.5 disposed by other methods). The amount of MSW produced in England was 28.8 million tonnes (Mtonnes), which corresponds to 587 kilograms per inhabitant. The majority of the MSW was landfilled (22 Mtonnes, or 77%), followed by recycling and composting (3.7 Mtonnes, 13%) and 9% of MSW was incinerated (2.6 Mtonnes).
                          Table 1

                          Waste Generation and Management in Italy, Slovakia and England in 2001.

                          Outcome

                          Italy

                          Slovakia

                          England

                           

                          Thousands tons

                          %

                          Thousands tons

                          %

                          Thousands tons

                          %

                          Landfill

                          17910

                          56

                          1192

                          78

                          22180

                          77

                          Incineration

                          2590

                          8

                          65

                          4

                          2590

                          9

                          Recycled/composted

                          7650

                          24

                          76

                          5

                          3740

                          13

                          Other

                          3790

                          12

                          191

                          13

                          290

                          1

                          Total

                          31940

                          100

                          1524

                          100

                          28800

                          100

                          MSW generation per capita/kg

                          560

                           

                          283

                           

                          587

                           

                          Population living close to incinerators and landfills

                          Table 2 shows the characteristics of the populations living close to the incinerators in the three countries. In Italy, more than a million people were included, with 9,010 newborns; less affluent social classes were over-represented compared to the national reference (25% in class V (deprived) and 12.6% in class I (less deprived), where classes are quintiles). A total of 64.4% of residents within 3 km were located in the 2-3 km band zone. Only 16,000 people lived close to the two incinerators in Slovakia. Also in this case, most of the residents lived in the farthest away circle. Contrary to Italy, the social class distribution around the two plants in Slovakia was skewed toward a higher social class. In England, approximately 1,200,000 people lived around the 11 incineration plants, mostly in the 2-3 km circular zone, and the social class distribution was strongly skewed towards deprivation (55% in class V (deprived) versus 3% in class I (less deprived).
                          Table 2

                          Characteristics of residents living within 3 km of an incinerator in Italy, Slovakia and England, 2001.

                          Variables

                          Italy

                          Slovakia

                          England

                           

                          N

                          %

                          N

                          %

                          N

                          %

                          Total

                          1060569

                           

                          16409

                           

                          1203208

                           

                          Sex

                                

                             males

                          511831

                          48.3

                          8039

                          49.0

                          592817

                          49.3

                             females

                          548738

                          51.7

                          8370

                          51.0

                          610391

                          50.7

                          Age (years)

                                

                             0

                          9010

                          0.8

                          176

                          1.1

                          16425

                          1.4

                             1-14

                          123061

                          11.6

                          2914

                          17.8

                          233047

                          19.4

                             15-44

                          435825

                          41.1

                          7795

                          47.5

                          569850

                          47.4

                             45-64

                          289430

                          27.3

                          4337

                          26.4

                          229133

                          19.0

                             65+

                          203243

                          19.2

                          1187

                          7.2

                          154753

                          12.9

                          Area-based socioeconomic status

                              

                             I (high)

                          133211

                          12.6

                          9127

                          55.6

                          35498

                          3.0

                             II

                          159735

                          15.1

                          386

                          2.4

                          76359

                          6.3

                             III

                          223059

                          21.0

                          1600

                          9.8

                          150253

                          12.5

                             IV

                          257009

                          24.2

                          4856

                          29.6

                          274692

                          22.8

                             V (low)

                          264401

                          24.9

                          414

                          2.5

                          666406

                          55.4

                             missing information

                          23154

                          2.2

                          26

                          0.2

                           

                          0.0

                          Period of start of the plant

                              

                             1960-1970

                          81586

                          7.7

                          0

                          0.0

                           

                          0.0

                             1971-1980

                          127750

                          12.0

                          14240

                          86.8

                          533915

                          44.4

                             1981-1990

                          301950

                          28.5

                          2169

                          13.2

                           

                          0.0

                             1991-2001

                          549283

                          51.8

                          0

                          0.0

                          669293

                          55.6

                          Distance from the plant

                               

                             0-1 Km

                          50990

                          4.8

                          221

                          1.3

                          95179

                          7.9

                             1-2 km

                          326798

                          30.8

                          3433

                          20.9

                          416987

                          34.7

                             2-3 km

                          682781

                          64.4

                          12755

                          77.7

                          691042

                          57.4

                          Table 3 shows the characteristics of the population living close to landfills in the three countries. The statistics for Italy were calculated for 118 sites in five regions and then extrapolated to the national level that included 619 sites. In Italy, more than 1,350,000 people were included (11,766 newborns); the social class distribution was skewed towards more deprivation (26% in class V (deprived) versus 13% in class I (less deprived)). The majority of residents within 2 km (85.7%) were located in the 1-2 km circular zone. A total of 328,869 people lived close to the 121 landfill plants in Slovakia. Also in this case, most of the residents lived in the longest circle farther away. In England, a total of 1,425,350 people lived close to the 232 geocoded landfills (including 16,242 newborns), especially in the 1-2 km circular area, and the social class distribution was skewed towards deprivation (20% in class V (deprived) versus 2.5% in class I (less deprived)), although the interpretation is difficult for the large proportion of the population with missing data on socioeconomic status.
                          Table 3

                          Characteristics of residents living within 2 km of landfills in Italy, Slovakia and England 2001.

                           

                          Italy observed data*

                          Italy estimated data**

                          Slovakia

                          England

                          Total

                          257513

                           

                          1350852

                           

                          328869

                           

                          1425350

                           

                          Sex

                                  

                             Males

                          125750

                          48.8

                          659655

                          48.8

                          159822

                          48.6

                          694137

                          48.7

                             females

                          131763

                          51.2

                          691197

                          51.2

                          169047

                          51.4

                          731213

                          51.3

                          Age (years)

                                  

                             0

                          2243

                          0.9

                          11766

                          0.9

                          3285

                          1.0

                          16242

                          1.1

                             1-14

                          32801

                          12.7

                          172066

                          12.7

                          59450

                          18.1

                          260043

                          18.2

                             15-44

                          107244

                          41.6

                          562577

                          41.6

                          156109

                          47.5

                          580430

                          40.7

                             45-64

                          67971

                          26.4

                          356560

                          26.4

                          76617

                          23.3

                          344290

                          24.2

                             65+

                          47254

                          18.4

                          247883

                          18.4

                          33408

                          10.2

                          224345

                          15.7

                          Area-based socioeconomic status

                               

                             I

                          34252

                          13.3

                          179678

                          13.3

                          79591

                          24.2

                          35277

                          2.5

                             II

                          38715

                          15.0

                          203090

                          15.0

                          81172

                          24.7

                          254972

                          17.9

                             III

                          57801

                          22.4

                          303210

                          22.4

                          74349

                          22.6

                          266629

                          18.7

                             IV

                          59320

                          23.0

                          311179

                          23.0

                          53893

                          16.4

                          271786

                          19.1

                             V

                          67339

                          26.1

                          353244

                          26.1

                          39855

                          12.1

                          286964

                          20.1

                             missing information

                          86

                          0.0

                          451

                          0.0

                          9

                          0.0

                          309722

                          21.7

                          Distance from the plant

                                

                             0-1 Km

                          36716

                          14.3

                          192603

                          14.3

                          59522

                          18.1

                          216938

                          15.2

                             1-2 km

                          220797

                          85.7

                          1158249

                          85.7

                          269347

                          81.9

                          1208412

                          84.8

                          *Only 118 out of 619 landfills were geocoded for Italy; ** estimation is based on data from the 118 landfills

                          Table 4 shows the results of the application of the local air dispersion model. Population-weighted additional exposure to PM10 and NO2 in 2001 is shown together with standard deviation and percentiles. The estimates for Italy and England derive from models with measured emissions (1st method) or national limits (2nd method). The additional contribution to PM10 (using the national limit value) is 0.0114 μg/m3 for Italy, 0.0078 μg/m3 for Slovakia, and 0.0143 μg/m3 for England. The additional contribution to NO2 (using the national limit value) is 0.2271 μg/m3 for Italy, 0.1542 μg/m3 for Slovakia, and 0.2855 μg/m3 for England. The use of measured emission values had a strong impact on the estimate for PM10 (eg. 0.0030 μg/m3 for Italy) but a lower impact for NO2 (e.g. 0.1944 μg/m3 for Italy).
                          Table 4

                          Results of the application of the local air dispersion model for PM10 and NO2 around 40 incinerators in Italy, 2 incinerators in Slovakia and 11 incinerators in England.

                           

                          Italy

                          Slovakia

                          England

                          PM 10 (measured data)

                             

                          Mean (SD)

                          0.0030 (0.0040)

                          na

                          0.0016 (0.0010)

                          25% percentile

                          0.0010

                          na

                          0.0009

                          50% percentile

                          0.0016

                          na

                          0.0014

                          75% percentile

                          0.0032

                          na

                          0.0020

                          PM 10 (national limits)

                             

                          Mean (SD)

                          0.0114 (0.0151)

                          0.0078 (0.0037)

                          0.0143 (0.0083)

                          25% percentile

                          0.0038

                          0.0066

                          0.0084

                          50% percentile

                          0.0061

                          0.0075

                          0.0124

                          75% percentile

                          0.0120

                          0.0082

                          0.0181

                          NO 2 (measured data)

                             

                          Mean (SD)

                          0.1944 (0.2583)

                          na

                          0.1346 (0.1056)

                          25% percentile

                          0.0658

                          na

                          0.0602

                          50% percentile

                          0.1050

                          na

                          0.1040

                          75% percentile

                          0.2060

                          na

                          0.1807

                          NO 2 (national limits)

                             

                          Mean (SD)

                          0.2271 (0.3018)

                          0.1542(0.0747)

                          0.2855 (0.1666)

                          25% percentile

                          0.0769

                          0.131

                          0.1690

                          50% percentile

                          0.1220

                          0.149

                          0.2490

                          75% percentile

                          0.2400

                          0.163

                          0.3612

                          Population-weighted exposure to PM10 and NO2 in 2001 (data in μg/m3).

                          na: data not available

                          When PM10 and NO2 population-weighted exposure levels were examined by selected population characteristics, no differences were found for gender and age but higher exposure values were found among those of lower socioeconomic status in Italy and England (not in Slovakia) (data not shown).

                          Health impacts due to incinerators

                          Table 5 shows the estimated number of additional cancer incident cases in the three countries for the period 2001-2050 as a result of exposure before 2001 (past exposure) and during 2001-2020 (current exposure). In Italy, an additional number of approximately 90 cases per year will be attributable to past exposure up to 2020 and then the number will decline to a minimum of 1.6 in 2050. On the other hand, the annual number of cases due to current exposure increases to 11 in 2020 and then will decline to 0 in 2050. In total, 2,729 additional cancer cases will be attributable to incinerators in Italy during 2001-2050 and the vast majority of them will be due to exposure before 2001. The total number of cancers attributable to exposure during 2001-2020 is 189.
                          Table 5

                          Estimated number of additional cancer cases in the three countries as result of exposure to incinerators before 2001 (past exposure) and during 2001-2020 (current exposure).

                           

                          Italy

                          Slovakia

                          England

                           

                          Additional cases

                          95% CI

                          Additional cases

                          95% CI

                          Additional cases

                          95% CI

                          Annual cases due to exposure before 2001 (Past exposure)

                            

                          2001

                          88

                          76 - 101

                          0.82

                          0.71 - 0.94

                          33

                          28 - 38

                          2010

                          92

                          79 - 105

                          0.85

                          0.73 - 0.98

                          36

                          31 - 41

                          2020

                          89

                          76 - 101

                          0.84

                          0.72 - 0.96

                          36

                          31 - 41

                          2030

                          28

                          24 - 32

                          0.28

                          0.24 - 0.32

                          12

                          10 - 13

                          2040

                          2.0

                          1.4 - 2.6

                          0.002

                          0.001 - 0.002

                          0.068

                          0.058 - 0.077

                          2050

                          1.6

                          1.1 - 2.1

                          0

                          0 - 0

                          0

                          0 - 0

                          2001-2050*

                          2540

                          2172-2896

                          23

                          20 - 27

                          1005

                          861-1149

                          Annual cases due to exposure during 2001-2020 (Current exposure)

                            

                          2001

                          0

                          0 - 0

                          0

                          0 - 0

                          0

                          0 - 0

                          2010

                          2.7

                          2.3 - 3.1

                          0.017

                          0.015 - 0.020

                          1.7

                          1.5 - 2.0

                          2020

                          11

                          10 - 13

                          0.071

                          0.061 - 0.081

                          7.1

                          6.1 - 8.1

                          2030

                          4.6

                          4.0 - 5.3

                          0.029

                          0.025 - 0.033

                          2.9

                          2.5 - 3.3

                          2040

                          0.051

                          0.044 - 0.05858

                          0

                          0 - 0

                          0.032

                          0.028 - 0.037

                          2050

                          0

                          0 - 0

                          0

                          0 - 0

                          0.0

                          0 - 0

                          2001-2050*

                          189

                          162-216

                          1.2

                          1.0 - 1.4

                          120

                          103 - 137

                          Total (Past + Current exposure)

                              

                          2001

                          88

                          76 - 101

                          0.82

                          0.71 - 0.94

                          33

                          28 - 38

                          2010

                          95

                          81 - 108

                          0.87

                          0.75 - 1.0

                          38

                          33 - 43

                          2020

                          100

                          86 - 114

                          0.91

                          0.78 - 1.0

                          43

                          37 - 49

                          2030

                          33

                          28 - 37

                          0.31

                          0.026 - 0.035

                          15

                          13 - 16

                          2040

                          2.1

                          1.4 - 2.7

                          0.002

                          0.001 - 0.002

                          0.100

                          0.086 - 0.114

                          2050

                          1.6

                          1.1 - 2.1

                          0

                          0 - 0

                          0.0

                          0 - 0

                          2001-2050*

                          2729

                          2334-3112

                          24

                          21 - 28

                          1125

                          964-1286

                          * Cumulative number of cases during the period 2001-2050

                          In Slovakia, less than one additional case per year is estimated for past exposure during the whole period whereas the estimate for current exposure is very low. In total, 24 additional cancer cases will be attributable to incinerators in Slovakia during 2001-2050 and the majority of them will be due to exposure before 2001. The total number of cancers attributable to exposure during 2001-2020 is 1.2.

                          In England, an additional number of approximately 36 cases per year will be attributable to past exposure up to 2020 and then the number will decline to 0 in 2050. On the other hand, the annual number of cases due to current exposure increases to 7 in 2020 and then will decline to 0 in 2050. In total, 1,125 additional cancer cases will be attributable to incinerators in England during 2001-2050 and the vast majority of them will be due to exposure before 2001. The total number of cancers attributable to exposure during 2001-2020 is 120.

                          Table 6 shows the total number of Years of Life Lost, YoLL (also the YoLL per 100,000 inhabitants and the number of lost days per person) in the three countries attributable to exposure to PM10 or NO2 from incinerators. In Italy, the impact is higher for NO2 (total YoLL 3,621, 341.4 per 100,000 inhabitants) than for PM10 (total YoLL 181, 17.16 per 100,000 inhabitants). In Slovakia, the total number of YoLL is also higher for NO2 (37, 226 per 100,000) than for PM10 (2, 12.2 per 100,000). Comparable results were available for England with a total impact similar to Italy (for NO2: total YoLL 3,966, 330 per 100,000 inhabitants; for PM10: total YoLL 199, 16.5 per 100,000 inhabitants). Overall, the maximum impact of incinerators is 1.25 days per each person in Italy, 0.82 days per person in Slovakia, and 1.20 days per person in England.
                          Table 6

                          Estimated number of Years of Life Lost (YLL) (follow up to 2050) in the three countries as result of exposure to PM10 and NO2 from incinerators.

                           

                          Italy

                          Slovakia

                          England

                           

                          Total YLL

                          YLL/100000

                          days/person

                          Total YLL

                          YLL/100000

                          days/person

                          Total YLL

                          YLL/100000

                          days/person

                          PM 10 (measured data)

                          5

                          0.47

                          0.002

                          n.a

                          n.a

                          n.a

                          22

                          1.83

                          0.007

                          PM 10 (national limits)

                          181

                          17.16

                          0.06

                          2

                          12.19

                          0.04

                          199

                          16.54

                          0.06

                          NO 2 (measured data)

                          3099

                          292.2

                          1.07

                          n.a

                          n.a

                          n.a

                          1871

                          155.5

                          0.57

                          NO 2 (national limits)

                          3621

                          341.4

                          1.25

                          37

                          225.5

                          0.82

                          3966

                          329.6

                          1.20

                          n.a.: not available

                          Health impacts due to landfills

                          Table 7 shows the health effects of landfills in the three countries as annual cases of congenital malformations and newborns of low birth weight during the period 2001-2030. It is expected that an average of 1.96 (95%CI = 0.98-2.94) additional cases of birth defects per year occur in Italy, 1.54 (0.77-2.31) in Slovakia and 2.7 (1.35-4.0) in England. The estimated number of infants of low birth weight is 42 (95%CI = 35-42), 13 (11-13), and 58 (49-58) cases per year for 30 years, respectively for the three countries.
                          Table 7

                          Estimated health effects of exposures to landfills in the three countries as annual cases of congenital malformations and newborns of low birth weight.

                           

                          Italy

                          Slovakia

                          England

                           

                          Expected cases

                          Additional cases

                          99% CI

                          Expected cases

                          Additional cases

                          99% CI

                          Expected cases

                          Additional cases

                          99% CI

                          All congenital anomalies

                          73

                          1.47

                          0.73 - 2.20

                          77

                          1.54

                          0.77 - 2.31

                          83

                          2.7

                          1.35 - 4.05

                          Neural tube defects

                          6

                          0.37

                          0.06 - 0.74

                          2

                          0.11

                          0.02 - 0.23

                          5

                          0.31

                          0.05 - 0.62

                          Hypospadias and epispadias

                          10

                          0.67

                          0.38 - 1.06

                          7

                          0.48

                          0.27 - 0.75

                          16

                          1.13

                          0.65 - 1.78

                          Abdominal wall defects

                          2

                          0.08

                          -0.09 - 0.24

                          12

                          0.60

                          -0.72 - 1.92

                          5

                          0.27

                          -0.32 - 0.86

                          Gastroschisis and exomphalos

                          2

                          0.27

                          0.05 - 0.51

                          12

                          2.16

                          0.36 - 4.09

                          5

                          0.85

                          0.14 - 1.61

                          Low birth weight

                          706

                          42.4

                          35.3-42.4

                          212

                          12.7

                          10.62 - 12.74

                          975

                          58.5

                          48.7 - 58.5

                          Discussion

                          We found that the three countries differed with regard to recycling, landfilling and incineration policies: in Slovakia and England landfills were the most important method of management whereas Italy had a higher proportion of recycling and use of mechanical and biological treatment (MBT) technologies; incineration was used equally in Italy and England. There was a sizeable population living close to management plants in the three areas (e.g. approximately 2% of the entire population in Italy live close to an incinerator while an additional 2.5% live close to a landfill). In both Italy and England, populations of lower socio-economic status were more likely to live closer to waste disposal sites.

                          A systematic review of the scientific literature [4] revealed that cancer incidence and adverse reproductive outcomes (congenital malformations and low birth weight) are the main health effects possibly related to incinerators and landfills, respectively. On the basis of the relative risks derived from published data, we found that the largest health impact from incinerators during the period of evaluation (2001-2050) was cancer incidence accounting for a small percentage increase over the background in the exposed population. The majority of the cancer cases are due to exposures occurring before 2001 whereas the relative impact from the current exposure pattern is smaller. The health burden is thus not amenable to intervention from new policies since those cancer cases will occur in any case. On the other hand, policies for future developments should consider that most of the health effects will be seen over several decades.

                          Confirming preliminary research in the UK [27], the additional contribution to the PM10 and NO2 background in proximity of incinerators estimated with air dispersion models is relatively small and roughly equivalent in the three countries. The application of the air dispersion model data to a life table analysis indicates that the maximum impact of incinerators on the overall mortality of the resident cohort will be from exposure to NO2. A few hundred Years of Life Lost per 100,000 people over the period 2001-2020 are expected to occur and the results are consistent in the three countries. However, the burden estimated with a large scale model for the entire European population should be added to the overall impact of incineration as the impact is widespread.

                          Our evaluation of the impact of landfills is driven from the relative lack of scientific knowledge related to health effects since only adverse reproductive disorders were considered. The studies reviewed and used for impact assessment are based on distance from the sources and they do not provide indication of the relevant etiological exposures. For incinerators, there was a variety of emissions from the stacks of these plants, including particles and gases, Polycyclic Aromatic Hydrocarbons, heavy metals and dioxins for which a link with cancer can be easily justified. On the other hand, a biological explanation for the health impacts from landfills is more difficult since the causative agents (e.g. heavy metals, Polycyclic Aromatic Hydrocarbons, solvents) and the exposure routes (inhalation, ingestion of drinking water, contact with contaminated soil) for reproductive outcomes have not been indicated. Without identifying the causative agent/s and a plausible mechanism of effect and exposure, the level of confidence of the health impact assessment is moderate. Overall, the overall estimated burden in each country consists of few cases of congenital malformations and low birth weight newborns.

                          There are several examples in the literature of risk assessment of a single or a limited number of waste management plants [27]. Results of risk assessment performed at the country level are more limited, although the ExternE methodology [28, 29] has been applied to estimate external costs of waste management. Rabl et al. [29] concluded that the only significant contributions come from direct emissions (of the landfill or incinerator) and from avoided emissions due to energy recovery (from an incinerator). Damage costs for incineration range from about 4 to 21 EUR tonne waste, and they are extremely dependent on the assumed scenario for energy recovery. For landfills the costs range from about 10 to 13 EUR tonne waste; it is dominated by greenhouse gas emissions because only a fraction of the CH4 can be captured. A complete assessment has been conducted in Singapore [30] but the main focus was on the environmental impact. Experiences of the health impact assessment in Europe are available from Ireland [31] and England [32]. The latest study provides a wide-ranging review focused on the environmental and health effects of MSW management.

                          Since lower socio-economic status is already associated with a higher risk of various negative health outcomes, an issue of environmental justice is present here because of the higher probability of exposure for less affluent people and their increased vulnerability. The situation is different for the two incinerators in Slovakia since they have an urban location and people living in urban areas in that country tend to have a higher socioeconomic profile. The issue has been extensively discussed in a recent paper [33] concluding that more effort should be made to investigate whether disadvantaged people are more vulnerable, i.e. risks differ in different social groups living in the same area. Notwithstanding this open question, decision makers should identify waste management policies that minimize their potential health impacts and unequal distribution.

                          Limitations and uncertainties

                          In our study, we assessed the potential impact of incinerators and landfill sites on the health of the nearby population. However, there are some key choices and limitations that are important to consider. There are substantial environmental emissions associated with waste transport that we did not consider in the present assessment. In addition, it was outside the scope of the present study to consider the potential health effects of composting and of mechanical and biological plants, the two main alternatives to incineration and landfilling [3]. Finally, we did not take into account the potential occupational health risks to waste management workers, in particular occupational accidents.

                          Our health impact assessment is characterized by a number of uncertainties that are typical of these exercises focusing on the long term-effects of prolonged, low-level exposures. We have listed the sources of uncertainties for each step of our evaluation and briefly summarize our confidence in the methods and results. The direction of the bias generated by these uncertainties is unknown.

                          Waste generation and management

                          As expected, there were inadequacies in data availability and reliability on MSW indicators as they are not uniform and not always available in the same format from published statistics. There were approximations in the available information on waste composition, classification of wastes is different in different countries, and we had high uncertainty concerning the amount and treatment of illegally disposed of waste. Overall, however, we have high confidence in the summary statistics that we have used and reported as they were available from reliable sources.

                          Population characteristics and exposure to air pollutants

                          While we had relatively high quality data for incinerators in the three countries, exact coordinates of landfills were difficult to find in Italy. In addition, we did face difficulties in estimating the exposed population because the location of the plant was approximate, the size of some landfills is not known, and the unit of the available population data (census block) does not fit our needs. Population data by age and gender, available locally, are based on census and projections for years beyond the census. Overall, we have very high confidence in the population data close to incinerators but our confidence in population data close to landfills is only moderate.

                          The results of the air dispersion models depend on the quality of the input data. We had operational data measured during recent years for some of the incinerators but only estimated emissions for some others. In addition, some plant characteristics were missing and had to be imputed. On the other hand, we could rely on high quality meteorological data for most of the plants and topography was also considered. Overall, we have a moderate confidence in the estimated air pollution concentrations close to incineration plants.

                          Relative risks and exposure-response functions

                          The application of excess-risk estimates based on distance from the plants has been problematic because of several difficulties in interpreting epidemiological studies. We have tried to address the issue in a transparent way by conducting a systematic evaluation, however, as underlined on several occasions above, we have moderate confidence in the excess risks used for the impact assessment of cancer cases and adverse reproductive outcomes. On the other hand, we have high confidence in the coefficients for long-term effects of PM10 and NO2 on mortality.

                          Quantification of the health impact

                          The quantification has been straightforward in terms of calculating excess cases as there are no difficulties in finding the appropriate health statistics and in taking into account the particular population characteristics near the facilities. However, the most difficult part is attributing the effect studied from old plants using old technologies to new facilities. We tried to evaluate the consequence of changing some of the parameters. Overall, we have moderate confidence in our method to estimate excess cancer cases and reproductive outcomes. On the other hand, we think the life table approach is rather robust, despite the assumptions made (time of the effect, stability of the population, constant mortality). Finally, there may be more health effects of living near waste facilities that were not considered for lack of suitable evidence. For example, a general loss of quality of life, stress, odours, poor perceived health, besides being important health endpoints per se, may also contribute to increases in morbidity and mortality. For all of these reasons, we have a moderate level of confidence in our quantification of the health impacts.

                          Our approach to evaluate the level of confidence deserves discussion. Explicitly addressing uncertainty is an important contribution of research because it clarifies what is known and unknown and thus stimulates further investigation. We rated the confidence we had according to a scheme adapted from the one used by the IPCC. The approach has been recently used to address the health impact of ultrafine particles in an expert elicitation process [34]. In this case, the likelihood of an effect of ultrafine particles on natural mortality was considered as moderate whereas the likelihood of and effect on asthma aggravation was considered vey high.

                          Conclusions

                          Past exposures from incinerators were likely to cause a sizeable health impact, especially for cancer, in Italy and England. However, the current impacts of landfilling and incineration can be characterized as moderate when compared to other sources of environmental pollution, e.g. traffic or industrial emissions, that have an importance on public health. The main results of the present study should be viewed in relation to the present debate within the EU and the Member States on the main policy issues related to waste management. Questions remain on the efforts that individual countries should make to reduce the overall amount of waste, and the appropriate targets to be met for recycling. Although waste to energy is gradually replacing old mass incineration, open issues remain also over the extent to which such policies should be introduced, given the possible alternatives [3]. There are several uncertainties and critical assumptions in our assessment model that are typical of a complex problem. However, we believe that it provides some insight into the relative health impact attributable to waste incineration and landfilling and that the model could potentially be useful as part of more articulated assessments for evaluating waste policy options, identifying knowledge gaps, and providing a framework for future comparative risk assessment.

                          List of abbreviations

                          EU: 

                          European Union

                          INTARESE: 

                          Integrated Assessment of Health Risks of Environmental Stressors in Europe

                          IPCC: 

                          Intergovernmental Panel on Climate Change

                          UK: 

                          United Kingdom

                          PM10: 

                          Particulate Matter

                          NO2: 

                          Nitrogen Dioxide

                          GIS: 

                          Geographic Information System

                          CH4: 

                          Methane.

                          Declarations

                          Acknowledgements

                          We acknowledge comments and useful criticism from Alfred Barnard (UCL), Anastasios Karabelas (CERTH), Alexandra Kuhn (USTUTT), Tomas Trnovec (RB-SMU), and Annunziata Faustini (ASL).

                          We thank the following persons for the case study in Italy: Antonio Lazzarino (ASL), Simona Milani (ASL), Giulia Cesaroni (ASL), Laura Erspamer (ARPA Emilia Romagna), Paolo Lauriola (ARPA Emilia Romagna), Giuseppe Viviano (ISS), Andrea Paina (APAT), Valeria Frittelloni (APAT), Nicola Caranci (Emilia-Romagna ASSR).

                          We thank the following persons for the case study in Slovakia: Nada Machkova (Slovak Environmental Agency - SEA), Alexander Jancarik (SEA), Katarina Paluchova (SEA), Jana Babjakova (RB-SMU), Vladimir Svabik (OLO BA), Katarina Stebnicka (KOSIT), Jozef Pecho (SHMI), Peter Teslik (SO SR), Peter Heidinger (SO SR).

                          This study was funded by the INTARESE project. INTARESE is a 5-year Integrated Project funded under the EU 6th Framework Programme - Priority 6.3 Global Change and Ecosystems (Contract No 018385). We thank Patrizia Compagnucci and Margaret Becker for their help in editing the manuscript.

                          Authors’ Affiliations

                          (1)
                          Department of Epidemiology, Regional Health Service, Lazio
                          (2)
                          Department of Epidemiology and Biostatistics, Imperial College
                          (3)
                          World Health Organization, Regional Office for Europe
                          (4)
                          World Health Organization, Regional Office for Europe
                          (5)
                          Department of Environmental Medicine, Slovak Medical University
                          (6)
                          Institute of Energy Economics and the Rational Use of Energy, University of Stuttgart
                          (7)
                          Environmental Health Reference Centre, Regional Agency for Environmental Prevention of Emilia Romagna

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