Short-term effects of PM10 on cause-specific mortality in municipalities across industrial sites in Italy

Introduction

Ambient air pollution is one of the most important environmental risk factors for the human health. Approximatively, 4.2 million of premature deaths occur each year worldwide due to air pollution exposure.¹ Moreover, 99% of total population is exposed to higher levels of air pollutants according to the World Health Organization (WHO) report released in 2022.² The environmental epidemiological research has clearly highlighted the harmful role of air pollution exposure on several outcomes, such as cardiovascular and respiratory mortality and morbidity.^3-7 
Air pollution is characterized by several pollutants produced by anthropogenic factors (vehicular traffic, industrial and agricultural activities, biomass burning, etc.) and natural sources (desert dust, forest fires, and rock erosion). Particulate matter (PM) is one of the most investigated and harmful air pollutants, which is also classified as Group 1 carcinogen by the International Agency for the Cancer Research (IARC).⁸ PM source can vary (traffic, civil heating, industries, etc.), determining the different toxicity and the ability to cause health effects in exposed populations.
Industrial activities, especially those involved in chemical and plastics manufacturing or processing, release into the atmosphere residuals of chemical elements which may be harmful to human health.⁹ Moreover, people residing near industrial areas are disproportionately affected by emissions and the subsequent atmospheric dispersion of air pollutants, with a possible effect on their health. Furtherly, such a population is often characterized by socioeconomic deprivation and personal behaviours and lifestyles (diet, alcohol consumption, smoking, etc.) that cause a general worsening of the health status.^10-14 
In addition to the higher toxicity of the mixture due to industrial emissions, evidence shows that the overall concentrations of air pollutants around the industrial sites are higher than in the nearby areas.^15,16 For a long time, it was not possible to fully evaluate the exposure of the population residing near industrial sites due to the spatial and temporal heterogeneity in the concentration field produced by the industrial facilities, as a result of emission parameters and local meteorology. The number of monitoring stations is often unable to detect such heterogeneities, which instead can be shown by specific dispersion models or ad hoc measurement campaigns, although long-time estimations are time- and cost-consuming. Recently, satellite data allows for the estimation of air pollution exposure also in areas where data lacked^17,18 for a long-time period. In Italy, the recent availability of such satellite models based on machine-learning approach has been applied to short-term epidemiological studies on several health outcomes,^5,19-21 mainly within the framework of the BEEP (Big data in Environmental and occuPational epidemiology) project (2017-2020).^5,21-23
Daily air pollution has been linked with mortality, mainly from urban studies^7,24 or nationwide analysis.¹⁹ Associations in non-urban areas, where industrial activities are often located, are still lacking or controversial,¹⁴ above all because factors such as the socioeconomic deprivation – often characterizing residents in industrial areas – act together with environmental pollution. In Italy, some studies have been conducted about the short-term exposure to PM and mortality/morbidity outcomes.^5,19,20,25 The authors indicated a positive association between exposure and outcomes, but more evidence is needed for specific settings, as cities near industrial sites. 
Recently, BIGEPI (Use of BIG data for the evaluation of the acute and chronic health Effects of air Pollution in the Italian population) project – a multicity study conducted in Italy – aimed to evaluate short- and long-term health effects of pollutants on multiple health endpoints at the national level in Italy. In such a framework, the association between daily concentrations of PM₁₀ and mortality of residents in municipalities near industrial sites in Italy was investigated. 
Moreover, the possible effect-modification with several individual factors, such as age and sex and area-level information as the type of facility, was studied on the main question of this research. 

Material and methods

Study area

The information used was gained by the European Pollutant Release and Transfer Register (EPR-TR) database, which provides data on the emissions of over 30,000 industrial plants located in the European Union (EU).²⁶ Industrial plants with relevant combustion processes and release into the atmosphere from a point emission source (chimney) were selected (Figure S1, see online Supplementary materials). The chosen plants were geocoded using the coordinates provided by the +EPR-TR database using the ArcGis software. The considered study areas are all the municipalities that fell – even with a portion of its territory – within a buffer of 4x4 km² or within a buffer of 8x8 km² around each industrial site. The list and location of the sites are reported Table S1 and Figure S1 (online Supplementary materials). 

Exposure assessment

Daily exposure to PM₁₀ concentrations during the years 2006-2015 was obtained by satellite data and elaborated by machine-learning approach, as described elsewhere.^17,18 Daily information about observed PM₁₀ data was collected using a national network of fixed monitors (~500) with more than 75% of complete data provided by the Institute for Environmental Protection and Research (ISPRA) and spatiotemporal predictors for every km² of Italy. Those data were combined by using a three-stage procedure based on the framework of the machine-learning approach, i.e., the random-forest models. Results were validated with a 10-fold cross-validation, achieving an R² of 0.75. Finally, all PM₁₀ predicted values for each day for 1 km² were collapsed to obtain a daily average of PM₁₀ concentration at municipality spatial resolution by using geo-processing techniques.

Health data

Aggregated information about cause-specific mortality for each municipality and day during the period 2006-2015 was retrieved from the Italian Ministry of Health. Specifically, the health outcomes under study were:

• non-accidental conditions (ICD-9 code: 0-799);
• cardiovascular diseases (ICD-9 code: 390-459), and respiratory diseases (ICD-9 code: 460-519). Data were stratified by sex and age class (0-64, 65-74, 75-84, and 85+ years) only for non-accidental mortality, because of data availability.

Statistical analysis

Mean values and standard deviations described continuous data, while frequencies and percentages were used to describe categorical data. A two-stage approach based on time-series study design was applied to elaborate on the relationship between daily exposure to PM₁₀ and cause-specific mortality in the study areas. In the first stage, over-dispersed Poisson regressions was applied in each municipality, where the municipality-specific daily count of deaths was the outcome variable and the (lagged) air pollutant was the exposure term. The models were adjusted for time trends, temperature, holidays, and population decrease. 
To account for seasonality and long-term trends, models was adjusted for time-trends adding a term modelled by natural splines with 6 degrees of freedom (dof) per year. Temperature was considered as apparent temperature,²⁷ a measure of the human discomfort composed by air temperature and relative humidity, subdivided into warm and cold temperatures based on the city-specific median values, where warm temperatures (all values below the median were fixed at the median value) were inserted as lagged 0-1 values  – equalling to the average between the same day and day before values – in the model by natural splines with 2 dof; cold temperatures (all values above the median are fixed as equal to the median value) were added as lagged 1-6 values with natural splines with 3 dof. Data about air temperature and relative humidity were retrieved from the Italian Air Force Meteorological Service. National holidays were adjusted for by a binary term, where 1 is assigned for each national holiday day and 0 for other days. Summer population decreases were considered as a three-level variable, where 2 is assigned to the 2 weeks period around the 15^th August, 1 to the period from 1^st July to 31^st August (except for days of Mid-August) and 0 to the other days. A detailed description is provided in the online Supplementary material. Influenza epidemics was not added in the list of confounding variables, as previously done in similar studies,^5,25 because this variable was not associated and its inclusion caused instability in the models. Exposures to PM₁₀ were considered at three different lags to evaluate different possible effects. Accordingly with previous evidence,^24,28,29 lag 0-1, 2-5, and 0-5 days were analysed to estimate, respectively, the immediate, delayed, and prolonged PM10-related effects. In the second stage, the city-specific risk estimates were pooled together by using a random-effect metanalysis.
In addition, possible effect modification was evaluated for non-accidental mortality by individual characteristics (sex and age class) and type of industrial plant considered. Specifically, the analyses were stratified by chemical, energy (thermal power, oil and gas refineries), metals and plastic, and minerals extraction sites. Effect modifiers were evaluated by considering the variable-specific count of deaths for each stratum of the modifier analysed (e.g., for each age class, for both sexes, etc.). P-value of heterogeneity was applied to test possible effect modification.
Finally, as a sensitivity analysis, the main approach was applied in the study area defined by the 8-km² buffers around the industrial sites.
Results were expressed as percent change in risks (% change) and relative 95% confidence intervals (95%CI) per 10 unit increases in PM₁₀ concentrations.
Analyses were conducted using R statistical software³⁰ and ArcGis³¹ as a geographical tool.

Results

A total of 61 industrial plants was selected – with chimneys and consequent possible impact on air quality – throughout the Italian territory. Among those, 48 were energy production plants, 7 minerals production plants, 3 metals production plants, and 3 chemical plants. The plants were mainly located in Northern Italy (37%), while 17% were located in the Southern Italy. The full list of the industrial sites is provided in the online Supplementary materials. 
When considering a buffer of 4x4 km² around each industrial facility, 100 municipalities were selected, with a total population of 1,688,679 persons (50.4% in Northern, 32.4% in Central, 17.2% in Southern Italy). Differently, when considering a buffer of 8x8 km² around each industrial facility, the population under study was 7,135,874 persons (39.6% in Northern, 46.9% in Central, 13.5%, in Southern Italy), distributed in 199 municipalities. 
In the period 2006-2015, over the whole study area, an average daily exposure of 28.4 µg/m³ with a standard deviation equal to 16.9 µg/m³ was estimated. As for the buffer of 4x4 km², a total of 568,804 deaths were observed for non-accidental causes across the whole study area (Table 1).

Among those deaths, there were 207,730 events for cardiovascular casuses and 39,096 for respiratory causes. Almost 70% of subjects were older than 75 years and 51% were females.
Table 2 shows the results of the pooled associations between PM₁₀ short-term exposure and cause-specific mortality over the whole study area. Positive percent changes of risk were observed for non-accidental mortality at each lag investigated, up to 1.75% (95%CI 0.82; 2.69) per 10 unit increases in PM₁₀. Positive associations were also found for respiratory mortality with estimates up to 7.89% (95%CI 0.16; 16.23) for respiratory causes at lag 0-1 and for cardiovascular mortality. Sites-specific estimates are reported in Figure S2 (see online Supplementary materials) for the association between PM₁₀ at lag 0-5 and cause-specific mortality.

Results of effect modification for the type of industry were reported in Table 3. Higher effects were observed in municipalities around metal and plastic production sites, especially for cardiovascular mortality, where positive associations at lag 0-5 (8.93%; 95%CI 3.43; 14.71) and at lag 2-5 (6.46%; 95%CI 2.05; 11.06) were observed; while negative associations were observed for chemical plants. Only associations in municipalities located near energy plants, which account for the majority of the study area, reported positive associations for all-cause mortality (at lag 0-5: 1.39%; 95%CI 0.50; 2.29). 

Figure 1 shows the results of the effect modification for individual characteristics. No differences emerged at any lag for either age-class or sex covariates.

Table 4 reports the estimates related to the secondary analyses, where an 8x8-km² buffer around each plant is considered as the study area. The number of municipalities included is 199, almost doubling the area under study when compared to the main approach. In this case, no effect was observed for any outcome at each lag considered.

Discussion

the association between daily exposure to PM₁₀ and cause-specific mortality in areas in proximity of industrial sites in Italy during the 2006-2015 period was explored. Positive associations were found between PM₁₀ exposure and non-accidental and respiratory mortality, especially at immediate lags. No detectable differences emerged by effect modification analysis. To the Authors’ knowledge, this is one of the few studies which focused on the short-term effects of PM exposure on industrial areas, while most of the evidence were produced about long-term effects^32-34 or in epidemiologic studies based on mortality data. 
Recently, a similar study has been conducted by the Authors in Italy about the short-term effects of several pollutants – PM₁₀ included – on cause-specific mortality during the 2013-2015 period.¹⁹ The results found were similar to those reported in the present study, especially for non-accidental and cardiovascular mortality, with estimates equal to 1.15 (95%CI 0.89; 1.42) and 0.97 (95%CI 0.50; 1.45) per 10-unit increases in PM₁₀ lagged 0-1 daily concentrations. Instead, results about respiratory mortality were lower (2.29 vs 7.89) than those of the study presented in this paper. In the previous case, all the Italian municipalities were considered; in the present study, the focus is only on the Italian municipalities located near industrial sites. The discrepancies about mortality for respiratory causes between the two studies might be explained by differences in the PM exposure and PM composition, but this hypothesis should be evaluated with appropriate PM source apportionment studies and monitoring campaigns.^16,35 Furthermore, a recent study highlighted a clear effect of being exposed to daily levels of PM₁₀ and all-cause mortality in Italy for different type of municipalities (urban, suburban and rural).²⁵ The authors found an overall 1.47% (95%CI 1.15; 1.79) change in risk for 10-unit increase in lagged 0-5 PM₁₀ concentrations in Italy, while similar estimates were reported for suburban and rural municipalities, which are more similar to the setting of the present study. In the current study, an increase of 1.75% was observed in all-cause mortality, considering the same lag and the same increment. Worldwide, a study conducted over 652 US cities reported positive associations between lagged 0-1 PM₁₀ exposure and all-cause, cardiovascular, and respiratory mortality,³⁶ with percent changes of risk equal to 0.44% (95%CI 0.39; 0.50), 0.36% (95%CI 0.30; 0.43), and 0.47% (95%CI 0.35; 0.58), respectively. 
In the current study, the municipalities considered were only the ones located near industrial sites, which might have been characterized by a frailer population, compared to the general population, due to a long-term exposure to industrial compounds; but this hypothesis is only speculative as the evidence is still limited. Out of 61 plants selected for this study, 16 are located in areas defined by the Italian Ministry of Environment as Sites of National Interests for Remediation:³⁷ they are included in the Italian database of the SENTIERI Project.³⁸ Some studies identified associations between living in proximity to polluted sites and adverse health outcomes, such as reproductive outcomes, malignancies, and congenital abnormalities.^13,14,39,40 For example, exposure to heavy metals, which might be released by metals and plastic facilities, has an impact on human health, especially on the female reproductive system.¹¹ Recently, SENTIERI has estimated the health impact assessment of long-term exposure to PM10 on the municipalities involved in the study.⁴¹ The authors calculated that 6% of total deaths might be attributable to PM exposure considering the whole municipality area, while they found 7% of total deaths attributable to PM exposure when they focused on the 4-km² buffer around the industrial site. These findings show how air pollution exposure plays a critical role in the health status of the resident population around the industrial sites. However, these numbers referred to chronic effects, while short-term exposure had not been investigated before. 
The composition of PM in proximity to industrial areas might be very different compared to the mixture in urban or rural sites.³⁵ Some chemical and metals compounds might increase the toxicity of PM.⁴² Some studies demonstrated that a higher presence of metals and PAHs in PM mixtures is related to adverse health outcomes.¹¹ This might support the results obtained in the secondary analyses, where estimates for metals and plastic sites are worse than those for other types of industrial facilities, even though the statistical power is limited. 
The link between breathing in particulate matter and its impact on health, including mortality, has been extensively studied in recent years.^43-45 Several mechanisms for the effect have been suggested. It is worth noting that a recent clinical study provided evidence that increased exposure to PM can stimulate the human central nervous system, leading to the release of hormones such as glucocorticoids, corticotropin-releasing hormone, and adrenocorticotropic hormone.⁴⁶ These mechanisms play a role in physiological processes that contribute to elevated blood pressure and insulin resistance, both of which are associated with significant health implications.
In the current study, sex and age class did not show differences in the effects. Despite the evidence of higher effects in fragile populations, such as the elderly,⁴⁷ no association between PM and mortality in subjects over 65 years was found. There are two possible explanations. Firstly, a selection bias might be occurred, because only healthy subjects survived in a fragile setting. Second, the role of occupational exposure could not be considered. In fact, people working in an industry tend to live near the workplace. These factors might have led to overexposure and higher effect in younger subjects, compared to the elderly.
In the sensitivity analyses, where the focused is on a larger buffer (8 km²) to test the hypothesis that possible effects were limited to 4-km² area, similar estimates with much larger confidence intervals were observed. A possible hypothesis explanation might be that, by increasing the study area, the population actually exposed may be diluted, conveying a weaker signal. 
One of the main strengths of this study is the city-specific exposure by using satellite data for each km² of Italy and the use of the official European list of industrial sites to select the industrial facilities. 
However, some limitations should be pointed out. Firstly, the definition of a fixed area of 4x4 km² (or 8x8 km² in the sensitivity analysis) across each site to define the study population made the Authors not able to detect the real dispersion of the pollutants around the sites, which mainly depends on several atmospheric phenomena, such as the wind (intensity and direction), as well as on the temperature of emitted pollutants, which increases their buoyancy. In addition, this study, by using exposure data derived by a national-scale machine-learning algorithm, based on spatiotemporal predictors, might have underestimated the contribution of local-scale phenomena, like those produced by an industrial facility. There is also a lack of information about PM_2.5 exposure for the same study period, which could have been interesting to investigate. However, PM₁₀ is more directly influenced by industrial activity, in contrast to PM_2.5, which makes it a relevant pollutant for this analysis, despite data availability being a significant limitation. 
Secondly, municipalities under study are characterized by low population size and low number of adverse events; thus, they do not yield a sufficient statistical power in order to detect a clear effect, especially for the secondary analyses. Furthermore, the occupational history of the residents has not been considered, but only residential exposure, which might have led to an underestimation of the exposure and the effect, has been taken into account. At last, only partial information about cause-specific mortality for age and sex was available, not allowing to run effect modification analyses on those outcomes.

Conclusions

This research explored the association between daily exposure to PM₁₀ and cause-specific mortality in municipalities around some Italian industrial sites. Positive associations and some suggestions of differential effects by different type of industrial sites, with higher estimates for metal and plastic industries, has been found. These results could be valuable for various stakeholders, especially policymakers, in designing specific policies for each industrial site. For example, conducting deeper local analysis for specific facilities with the evaluation of the health impact assessment in different scenarios could be insightful. Furthermore, consideration should be given to the possibility of extending this study to encompass all industrial sites in Italy or integrating more information regarding pollutant dispersion and the socioeconomic characteristics of the populations residing near these industries. This would contribute to a more comprehensive understanding of the environmental hazards to which subjects are truly exposed.

Conflicts of interest: none declared.

Funding: this work was supported by the National Institute for Insurance against Accidents at Work (INAIL) within the BIGEPI project (project No. 46/2019).

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