Air quality, metal(loid) sources identification and environmental assessment using (bio)monitoring in the former mining district of Salsigne (Orbiel valley, France).

The former mining district of Salsigne is situated in the Orbiel valley. Until the 20th century, it was the first gold mine in Europe and the first arsenic mine in the world. Rehabilitation has been performed during the 20 years that followed closure of the mines and factories, which led to the accumulation of storage of several million tons of waste in this valley. Nevertheless, a detailed description of the air quality of this area is still missing. The goal of the present study is to evaluate atmospheric contamination in the valley and identify the potential sources of this contamination. Active monitors (particulate matter samplers) and passive bioindicators (Tillandsia usneoides) were placed in strategic sites including remote areas. Over the year 2022, we assessed the air quality using microscopic and spectroscopic techniques, as well as environmental risk indicators to report the level of contamination. Results indicate that the overall air quality in the valley is good with PM10 levels in accordance with EU standards. Elemental concentrations in the exposed plants were lower than reported in the literature. Among the different sites studied, Nartau and La Combe du Saut, corresponding to waste storage and former mining industry sites, were the most affected. Chronic exposure over 1 year was highlighted for Fe, Ni, Cu, Pb, Sb and As. Pollution Load Index and Enrichment Factors, which provided valuable information to assess the environmental condition of the valley's air, suggested that dust and resuspension of anthropogenic materials were the principle sources for most of the elements. Finally, this study also highlights that using T. usneoides could be a convenient approach for biomonitoring of metal (loid)-rich particles in the atmosphere within a former mining area, for at least one year. These results in turn allow to better understand the effects of chronic exposure on the ecosystem.

Personal exposure monitoring of fine and coarse particulate matter using exposure assessment models for elderly residents in Hong Kong.

This study investigated the determinants of personal exposures (PE) to coarse (PM2.5-10) and fine particulate matter (PM2.5) for elderly communities in Hong Kong. The mean PE PM2.5 and PM2.5-10 were 23.6 ± 10.8 and 13.5 ± 22.1 µg/m3, respectively during the sampling period. Approximately 76% of study subjects presented statistically significant differences between PE and ambient origin for PM2.5 compared to approximately 56% for PM2.5-10, possibly due to the coarse-size particles being more influenced by similar sources (road dust and construction dust emissions) compared to the PM2.5 particles. Individual PE to ambient (P/A) ratios for PM2.5 all exceeded unity (≥1), suggesting the dominant influences of non-ambient particles contributed towards total PE values. There were about 80% individual P/A ratios (≤1) for PM2.5-10, implying possible effective infiltration prevention of larger size particulate matter particles leading to dominant influences from the outdoor sources. The higher concentration of NO3- and SO42- in PM2.5-10 compared to PM2.5 suggests possible heterogeneous reactions of alkaline minerals leading to the formation of NO3- and SO42- in PM2.5-10 particles. The PE and ambient OC/EC ratios in PM2.5 (8.8 ± 3.3 and 10.4 ± 22.4, respectively) and in PM2.5-10 (6.0 ± 1.9 and 3.0 ± 1.1, respectively) suggest possible secondary formed OC from surrounding rural areas. Heterogeneous distributions (COD >0.2) between the PE and ambient concentrations were found for both the PM2.5 and PM2.5-10 samples. The calibration coefficient as the association between personal and surrogate exposure measure of PE to PM2.5 (0.84) was higher than PM2.5-10 (0.52). The findings further confirm that local sources were the dominant contributor to the coarse particles and these coefficients can potentially be used to estimate different PE to PM2.5 and PM2.5-10 conditions. A comprehensive understanding of the PE to determinants in coarse particles is essential to further reduce potential exposure misclassification.

Global population exposure to landscape fire air pollution from 2000 to 2019.

Wildfires are thought to be increasing in severity and frequency as a result of climate change1-5. Air pollution from landscape fires can negatively affect human health4-6, but human exposure to landscape fire-sourced (LFS) air pollution has not been well characterized at the global scale7-23. Here, we estimate global daily LFS outdoor fine particulate matter (PM2.5) and surface ozone concentrations at 0.25° × 0.25° resolution during the period 2000-2019 with the help of machine learning and chemical transport models. We found that overall population-weighted average LFS PM2.5 and ozone concentrations were 2.5 µg m-3 (6.1% of all-source PM2.5) and 3.2 µg m-3 (3.6% of all-source ozone), respectively, in 2010-2019, with a slight increase for PM2.5, but not for ozone, compared with 2000-2009. Central Africa, Southeast Asia, South America and Siberia experienced the highest LFS PM2.5 and ozone concentrations. The concentrations of LFS PM2.5 and ozone were about four times higher in low-income countries than in high-income countries. During the period 2010-2019, 2.18 billion people were exposed to at least 1 day of substantial LFS air pollution per year, with each person in the world having, on average, 9.9 days of exposure per year. These two metrics increased by 6.8% and 2.1%, respectively, compared with 2000-2009. Overall, we find that the global population is increasingly exposed to LFS air pollution, with socioeconomic disparities.

Exploring the effect of city size on carbon emissions: Evidence from 259 prefecture-level cities in China.

As a gathering place for human production activities, cities are the main places where energy consumption and carbon dioxide emissions occur. How to accurately measure city size and test the impact mechanism of city size on carbon emissions of different city levels is still controversial. This study uses the global nighttime light data to identify urban bright areas and built-up areas, and accordingly constructs the city size index of 259 prefecture level cities in China from 2003 to 2019. It avoids the problem of only considering the single index of population size or space size, and makes the measurement of city size more reasonable. We use a dynamic panel model to study the impact of city size on urban carbon emissions per capita, and discuss the heterogeneity of various cities under different population levels and economic development levels. The empirical results indicate that in the scale of cities in China showed a fluctuating growth trend in recent years. The city size index of most cities is clustered at medium and high values. The city size index of cities with different economic development levels and different population-scale levels shows obvious gradient differences but maintains an upward trend. The expansion of supercities (with a population of more than 5 million) introduces a drastic increase in carbon emissions. The carbon emissions growth caused by the expansion of cities that are classified as third tier and below is the smallest, while that caused by the expansion of cities that are classified as first-tier is the largest. The findings suggest differentiated emissions reduction suggestions for cities with different sizes.

Analysis on influencing factors of carbon emissions from China's pulp and paper industry and carbon peaking prediction.

China's pulp and paper industry (CPPI) has been always the main carbon emission source in recent years. However, the analysis on influencing factors of carbon emissions from this industry is insufficient. To address the issue, the CO2 emissions from CPPI are estimated in the period of 2005-2019, the driving factors of CO2 emissions are investigated by the logarithmic mean Divisia index (LMDI) method, the decoupling state of economic growth and CO2 emissions is determined by Tapio decoupling model, and finally, future CO2 emissions are predicted under four scenarios by the STIRPAT model to explore the potential of carbon peaking. The results show that CPPI exhibits a rapid increase and a fluctuating downward trend in CO2 emissions during the period of 2005-2013 and 2014-2019, respectively. The main promoting and inhibiting factors to the increase of CO2 emission are per capita industrial output value and energy intensity, respectively. There are five decoupling states of CO2 emissions and economic growth during the study period, and the CO2 emissions exhibit a weak decoupling state with the industrial output value growth in most years of the study period. It is very difficult to realize the carbon peaking goal by 2030 under the baseline and fast development scenarios. Therefore, efficient low carbon and strong low-carbon development policies are necessary and urgent for the realization of carbon peaking goal and the sustainable development of CPPI.

In the era of globalization, can renewable energy and eco-innovation be viable for environmental sustainability in BRICS economies?

Although energy is a necessary component of production and hence a contaminant, the environmental effect varies depending on the type of energy used. Renewable sources of energy can provide ecological advantages, particularly when contrasted with fossil fuels, which emit high levels of CO2 emissions. Thus, the research explores the impact of eco-innovation (ECO), green energy (REC), and globalization (GLOB) on the ecological footprint (ECF) in the BRICS nations using the panel nonlinear autoregressive distributed lag (PNARDL) technique between 1990 and 2018. The empirical results indicate that there is cointegration in the model. The results from the PNARDL show that a positive shift in renewable energy, eco-innovation, and globalization decreases the ecological footprint, while positive (negative) shifts in non-renewable energy and economic growth intensify the ecological footprint. The paper suggests several policy recommendations based on these results.

Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions.

There is a growing need to apply geospatial artificial intelligence analysis to disparate environmental datasets to find solutions that benefit frontline communities. One such critically needed solution is the prediction of health-relevant ambient ground-level air pollution concentrations. However, many challenges exist surrounding the size and representativeness of limited ground reference stations for model development, reconciling multi-source data, and interpretability of deep learning models. This research addresses these challenges by leveraging a strategically deployed, extensive low-cost sensor (LCS) network that was rigorously calibrated through an optimized neural network. A set of raster predictors with varying data quality and spatial scales was retrieved and processed, including gap-filled satellite aerosol optical depth products and airborne LiDAR-derived 3D urban form. We developed a multi-scale, attention-enhanced convolutional neural network model to reconcile the LCS measurements and multi-source predictors for estimating daily PM2.5 concentration at 30-m resolution. This model employs an advanced approach by using the geostatistical kriging method to generate a baseline pollution pattern and a multi-scale residual method to identify both regional patterns and localized events for high-frequency feature retention. We further used permutation tests to quantify the feature importance, which has rarely been done in DL applications in environmental science. Finally, we demonstrated one application of the model by investigating the air pollution inequality issue across and within various urbanization levels at the block group scale. Overall, this research demonstrates the potential of geospatial AI analysis to provide actionable solutions for addressing critical environmental issues.

Investigating the impact of air pollution in selected African developing countries.

Air pollution is a major challenge faced by most countries due to the continuous quest of industrialization leading to a high pollution, which Africa is not an exception. Nowadays countries are trying to regulate their carbon emission by transitioning from traditional methods to modern technologies. However, measures to reduce environmental pollution due to carbon emissions and PM2.5 do not receive rational attention and represent a serious threat to the continent. The aim of this study is to investigate the impact of the adoption of green innovation technologies on carbon and particulate matter (PM2.5) emission reduction in some of the most polluted African countries and its effect on the economic growth from 2000 to 2019. We based on cross-sectional dependency analysis, unit root test, and other robust tests for the data analysis since they are best fit and accurate for the data interpretation. The short- and long-term CS-ARDL empirical evidence showed a negative and significant impact of the adoption of green energy technology innovation on carbon and particulate matter emissions, implying that the adoption of green energy technology significantly reduces the emission of carbon and PM2.5 emissions. However, the results show that the use of non-renewable energies has a negative effect on environmental sustainability since it increases carbon and particle matter emissions. There is a need for the selected African countries to efficiently adopt ecofriendly technologies to reduce their carbon and PM2.5 emissions and take advantage of natural resources such as the constant sunny weather to implement the installation of solar panels as a source of energy and other technologies. Various recommendations have been made to effectively reduce CO2 and particulate matter emissions; future studies should be conducted on the implementation of the recommended policies.

Source apportionment of PM10 and health risk assessment related in a narrow tropical valley. Study case: Metropolitan area of Aburrá Valley (Colombia).

This study investigates spatio-temporal variations of PM10 mass concentrations and associated metal(oid)s, δ13C carbon isotope ratios, polycyclic aromatic hydrocarbons (PAHs), total organic carbon (TOC) and equivalent black carbon (eBC) concentrations over a half year period (from March 2017 to October 2017) in two residential areas of Medellín (MED-1 and MED-2) and Itagüí municipality (ITA-1 and ITA-2) at a tropical narrow valley (Aburrá Valley, Colombia), where few data are available. A total of 104 samples were analysed by using validated analytical methodologies, providing valuable data for PM10 chemical characterisation. Metal(oid)s concentrations were measured by inductively coupled plasma mass spectrometry (ICP-MS) after acid digestion, and PAHs concentrations were measured by Gas Chromatography-Mass Spectrometry (GC-MS) after Pressurised Hot Water Extraction (PHWE) and Membrane Assisted Solvent Extraction (MASE). Mean PM10 mass concentration ranged from 37.0 µg m-3 to 45.7 µg m-3 in ITA-2 and MED-2 sites, respectively. Al, Ca, Mg and Na (from 6249 ng m-3 for Mg at MED-1 site to 10,506 ng m-3 for Ca at MED-2 site) were the major elements in PM10 samples, whilst As, Be, Bi, Co, Cs, Li, Ni, Sb, Se, Tl and V were found at trace levels (< 5.4 ng m-3). Benzo[g,h,i] perylene (BghiP), benzo[b + j]fluoranthene (BbjF) and indene(1,2,3-c,d)pyrene (IcdP) were the most profuse PAHs in PM10 samples, with average concentrations of 0.82-0.86, 0.60-0.78 and 0.47-0.58 ng m-3, respectively. Results observed in the four sampling sites showed a similar dispersion pattern of pollutants, with temporal fluctuations which seems to be associated to the meteorology of the valley. A PM source apportionment study were carried out by using the positive matrix factorization (PMF) model, pointing to re-suspended dust, combustion processes, quarry activity and secondary aerosols as PM10 sources in the study area. Among them, combustion was the major PM10 contribution (accounting from 32.1 to 32.9% in ITA-1 and ITA-2, respectively), followed by secondary aerosols (accounting for 13.2% and 23.3% ITA-1 and MED-1, respectively). Finally, a moderate carcinogenic risk was observed for PM10-bound PAHs exposure via inhalation, whereas significant carcinogenic risk was estimated for carcinogenic metal(oid)s exposure in the area during the sampling period.

Spatio-Temporal Coupling Evolution of Urbanisation and Carbon Emission in the Yangtze River Economic Belt.

The distribution characteristics of urbanisation level and per capita carbon emissions from 2006 to 2019 were investigated by the ranking scale rule, using 108 cities in the Yangtze River Economic Belt of China. A coupling coordination model was established to analyse the relative development relationship between the two, and exploratory spatial-temporal data analysis (ESTDA) was applied to reveal the spatial interaction characteristics and temporal evolution pattern of the coupling coordination degree. The results demonstrate that: (1) The urbanisation level and per capita carbon emissions of the Yangtze River Economic Belt show a stable spatial structure of 'high in the east and low in the west'. (2) The coupling and coordination degree of urbanisation level and carbon emissions show a trend of 'decreasing and then increasing', with a spatial distribution of 'high in the east and low in the west'. (3) The spatial structure exhibits strong stability, dependence, and integration. The stability is enhanced from west to east, the coupling coordination degree has strong transfer inertia, and the spatial pattern's path dependence and locking characteristics show a trend of weak fluctuation. Therefore, the coupling and coordination analysis is required for the coordinated development of urbanisation and carbon emission reduction.

Air Pollution and Mortality at the Intersection of Race and Social Class.

BACKGROUND: Black Americans are exposed to higher annual levels of air pollution containing fine particulate matter (particles with an aerodynamic diameter of ≤2.5 µm [PM2.5]) than White Americans and may be more susceptible to its health effects. Low-income Americans may also be more susceptible to PM2.5 pollution than high-income Americans. Because information is lacking on exposure-response curves for PM2.5 exposure and mortality among marginalized subpopulations categorized according to both race and socioeconomic position, the Environmental Protection Agency lacks important evidence to inform its regulatory rulemaking for PM2.5 standards. METHODS: We analyzed 623 million person-years of Medicare data from 73 million persons 65 years of age or older from 2000 through 2016 to estimate associations between annual PM2.5 exposure and mortality in subpopulations defined simultaneously by racial identity (Black vs. White) and income level (Medicaid eligible vs. ineligible). RESULTS: Lower PM2.5 exposure was associated with lower mortality in the full population, but marginalized subpopulations appeared to benefit more as PM2.5 levels decreased. For example, the hazard ratio associated with decreasing PM2.5 from 12 µg per cubic meter to 8 µg per cubic meter for the White higher-income subpopulation was 0.963 (95% confidence interval [CI], 0.955 to 0.970), whereas equivalent hazard ratios for marginalized subpopulations were lower: 0.931 (95% CI, 0.909 to 0.953) for the Black higher-income subpopulation, 0.940 (95% CI, 0.931 to 0.948) for the White low-income subpopulation, and 0.939 (95% CI, 0.921 to 0.957) for the Black low-income subpopulation. CONCLUSIONS: Higher-income Black persons, low-income White persons, and low-income Black persons may benefit more from lower PM2.5 levels than higher-income White persons. These findings underscore the importance of considering racial identity and income together when assessing health inequities. (Funded by the National Institutes of Health and the Alfred P. Sloan Foundation.).

Long-term exposure to elemental components of fine particulate matter and all-natural and cause-specific mortality in a Danish nationwide administrative cohort study.

BACKGROUND: Fine particulate matter (PM2.5) is a well-recognized risk factor for premature death. However, evidence on which PM2.5 components are most relevant is unclear. METHODS: We evaluated the associations between mortality and long-term exposure to eight PM2.5 elemental components [copper (Cu), iron (Fe), zinc (Zn), sulfur (S), nickel (Ni), vanadium (V), silicon (Si), and potassium (K)]. Studied outcomes included death from diabetes, chronic kidney disease (CKD), dementia, and psychiatric disorders as well as all-natural causes, cardiovascular disease (CVD), respiratory diseases (RD), and lung cancer. We followed all residents in Denmark (aged ≥30 years) from January 1, 2000 to December 31, 2017. We used European-wide land-use regression models at a 100 × 100 m scale to estimate the residential annual mean levels of exposure to PM2.5 components. The models were developed with supervised linear regression (SLR) and random forest (RF). The associations were evaluated by Cox proportional hazard models adjusting for individual- and area-level socioeconomic factors and total PM2.5 mass. RESULTS: Of 3,081,244 individuals, we observed 803,373 death from natural causes during follow-up. We found significant positive associations between all-natural mortality with Si and K from both exposure modeling approaches (hazard ratios; 95% confidence intervals per interquartile range increase): SLR-Si (1.04; 1.03-1.05), RF-Si (1.01; 1.00-1.02), SLR-K (1.03; 1.02-1.04), and RF-K (1.06; 1.05-1.07). Strong associations of K and Si were detected with most causes of mortality except CKD and K, and diabetes and Si (the strongest associations for psychiatric disorders mortality). In addition, Fe was relevant for mortality from RD, lung cancer, CKD, and psychiatric disorders; Zn with mortality from CKD, RD, and lung cancer, and; Ni and V with lung cancer mortality. CONCLUSIONS: We present novel results of the relevance of different PM2.5 components for different causes of death, with K and Si seeming to be most consistently associated with mortality in Denmark.

Assessment of particulate matter (PM) in ambient air of different settings and its associated health risk in Haripur city, Pakistan.

Particulate matter (PM) is a major air pollutant causing serious health problems. The aim of the present study was to find out concentration of PM in ambient air and its associated health risk in Haripur city, Pakistan. Twenty-three samples were taken at various educational institutes, hospitals, recreational areas and industries in Haripur city. Concentration of PM2.5 (µg/m3) and PM10 (µg/m3) was measured with Youngteng YT-HPC 3000A portable PM counter. The results revealed that values of both PM2.5 and PM10 were above the permissible limits (35 µg/m3 for PM2.5 and 150 µg/m3 for PM10) set by Environmental Protection Agency Pakistan (Pak-EPA) in all the educational institutes, hospitals, recreational areas and industries investigated. Furthermore, significant (p<0.05) variation was found in the concentration of both PM2.5 and PM10 in all the educational institutes, hospitals, recreational areas, and industries studied. The concentration of PM2.5 was positively correlated with the concentration of PM10 in all the sampling sites. Therefore, from 1-14 scale standard of health index, the values of PM2.5 and PM10 exhibited that the ambient air quality of Haripur city Pakistan is under high risk. If the regulatory authorities such as Environmental Protection Agency, Health Department and Local Government monitor PM pollution in different settings of Haripur city, then a decrease can be possible in the pollution level. The remedies that can be taken to overcome the problem of ambient air pollution such as PM are plantation of trees at the sites where there are higher levels of air pollutants and use of masks on personal protection basis along with implementation of pollution control system in industries of Hattar Industrial Estate Haripur city, Pakistan.

A simulation-based assessment of the ability to detect thresholds in chronic risk concentration-response functions in the presence of exposure measurement error.

An important question when setting appropriate air quality standards for fine particulate matter (PM2.5) is whether there exists a "threshold" in the concentration-response (C-R) function, such that PM2.5 levels below this threshold are not expected to produce adverse health effects. We hypothesize that measurement error may affect the recognition of a threshold in long-term cohort epidemiological studies. This study conducts what is, to the best of our knowledge, the first simulation of the effects of measurement error on the statistical models commonly employed in long-term cohort studies. We test the degree to which classical-type measurement error, such as differences between the true population-weighted exposure level to a pollutant and the observed measures of that pollutant, affects the ability to statistically detect a C-R threshold. The results demonstrate that measurement error can obscure the existence of a threshold in a cohort study's C-R function for health risks from chronic exposures. With increased measurement error the ability to statistically detect a C-R threshold decreases, and both the estimated location of the C-R threshold and the estimated hazard ratio associated with PM2.5 are attenuated. This result has clear implications for determining appropriate air quality standards for pollutants.

Atmospheric pollution and mortality in Portugal: Quantitative assessment of the environmental burden of disease using the AirQ+ model.

In Portugal, data on mortality rate attributed to household and ambient air pollution are not reported due to shortness and irregularity of the available data series, and therefore, the disclosure of the national progress in reducing the number of deaths and illnesses from air contamination in exposures to multiple pollutants is incomplete. The present work describes the application of the AirQ+ model developed by the WHO to calculate how much of specific health outcomes is attributable to long-term exposure to atmospheric NO2, PM2.5, and O3 in the population of various municipalities in Portugal, from 2010 to 2019. Linear Mixed Models were used for data analysis and have shown that (i) approximately 5000 deaths per year are attributable to exposure to mixtures of NO2 and PM2.5; (ii) the spatial distribution of the proportion of deaths attributable to NO2, PM2.5 and O3 shows significant differences between locations, and (iii) that AirQ+ is a useful tool for the purpose of effective Public Health policymaking and reporting on the national progress to implement the 2030 Agenda.

West Oakland's Experience in Building Community Power to Confront Environmental Injustice Through California's Assembly Bill 617.

We explored how air quality management processes associated with Assembly Bill 617 (AB 617) in West Oakland, California, represent a shift in power relationships between government agencies and communities toward the goal of addressing legacies of environmental injustice. We drew from a statewide assessment of community engagement in AB 617's first year, and an analysis of the West Oakland AB 617 process. The first comprised 2 statewide surveys (n = 102 and n = 106), 70 key informant interviews, observation of all AB 617 first-year sites, and analysis of related planning documents. The second comprised 2 rounds of interviews (n = 22 and n = 23, with a total of 19 individuals) and extensive participant observation. Several factors are necessary for pursuing environmental justice: (1) invest in community partnerships and collaborations, (2) honor community knowledge and data, (3) ensure that community constituents share power in environmental governance, and (4) adopt explicit racial justice frameworks. Although still a work in progress, AB 617 offers important lessons for community and policy organizations nationwide engaged in environmental justice. (Am J Public Health. 2022;112(2):262-270. https://doi.org/10.2105/AJPH.2021.306592).

Long-term nitrogen dioxide exposure and cause-specific mortality in the U.S. Medicare population.

BACKGROUND: Since 1971, the annual National Ambient Air Quality Standard (NAAQS) for nitrogen dioxide (NO2) has remained at 53 ppb, the impact of long-term NO2 exposure on mortality is poorly understood. OBJECTIVES: We examined associations between long-term NO2 exposure (12-month moving average of NO2) below the annual NAAQS and cause-specific mortality among the older adults in the U.S. METHODS: Cox proportional-hazard models were used to estimate Hazard Ratio (HR) for cause-specific mortality associated with long-term NO2 exposures among about 50 million Medicare beneficiaries living within the conterminous U.S. from 2001 to 2008. RESULTS: A 10 ppb increase in NO2 was associated with increased mortality from all-cause (HR: 1.06; 95% CI: 1.05-1.06), cardiovascular (HR: 1.10; 95% CI: 1.10-1.11), respiratory disease (HR: 1.09; 95% CI: 1.08-1.11), and cancer (HR: 1.01; 95% CI: 1.00-1.02) adjusting for age, sex, race, ZIP code as strata ZIP code- and state-level socio-economic status (SES) as covariates, and PM2.5 exposure using a 2-stage approach. NO2 was also associated with elevated mortality from ischemic heart disease, cerebrovascular disease, congestive heart failure, chronic obstructive pulmonary disease, pneumonia, and lung cancer. We found no evidence of a threshold, with positive and significant HRs across the range of NO2 exposures for all causes of death examined. Exposure-response curves were linear for all-cause, supra-linear for cardiovascular-, and sub-linear for respiratory-related mortality. HRs were highest consistently among Black beneficiaries. CONCLUSIONS: Long-term NO2 exposure is associated with elevated risks of death by multiple causes, without evidence of a threshold response. Our findings raise concerns about the sufficiency of the annual NAAQS for NO2.

Long-term exposure to PM2.5 major components and mortality in the southeastern United States.

BACKGROUND: Long-term exposure to fine particulate matter (PM2.5) mass has been associated with adverse health effects. However, the health effects of PM2.5 components have been less studied. There is a pressing need to better understand the relative contribution of components of PM2.5, which can lay the scientific basis for designing effective policies and targeted interventions. METHODS: We conducted a population-based cohort study, comprising all Medicare enrollees aged 65 or older in the southeastern United States from 2000 to 2016, to explore the associations between long-term exposure to PM2.5 major components and all-cause mortality among the elderly. Based on well-validated prediction models, we estimated ZIP code-level annual mean concentrations for five major PM2.5 components, including black carbon (BC), nitrate (NIT), organic matter (OM), sulfate (SO4), and soil particles. Data were analyzed using Cox proportional hazards models, adjusting for potential confounders. RESULTS: The cohort comprised 13,590,387 Medicare enrollees and a total of 107,191,652 person-years. In single-component models, all five major PM2.5 components were significantly associated with elevated all-cause mortality. The hazard ratios (HR) per interquartile range (IQR) increase in exposure were 1.027 (95% CI: 1.025-1.030), 1.012 (95% CI: 1.010-1.013), 1.018 (95% CI: 1.017-1.020), 1.021 (95% CI: 1.017-1.024), and 1.004 (95% CI: 1.003-1.006) for BC, NIT, OM, SO4, and soil particles, respectively. While the effect estimate of soil component was statistically significant, it is much smaller than those of combustion-related components. CONCLUSION: Our study provides epidemiological evidence that long-term exposure to major PM2.5 components is significantly associated with elevated mortality.

Variability in the association between long-term exposure to ambient air pollution and mortality by exposure assessment method and covariate adjustment: A census-based country-wide cohort study.

BACKGROUND: Ambient air pollution exposure has been associated with higher mortality risk in numerous studies. We assessed potential variability in the magnitude of this association for non-accidental, cardiovascular disease, respiratory disease, and lung cancer mortality in a country-wide administrative cohort by exposure assessment method and by adjustment for geographic subdivisions. METHODS: We used the Belgian 2001 census linked to population and mortality register including nearly 5.5 million adults aged ≥30 (mean follow-up: 9.97 years). Annual mean concentrations for fine particulate matter (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and ozone (O3) were assessed at baseline residential address using two exposure methods; Europe-wide hybrid land use regression (LUR) models [100x100m], and Belgium-wide interpolation-dispersion (RIO-IFDM) models [25x25m]. We used Cox proportional hazards models with age as the underlying time scale and adjusted for various individual and area-level covariates. We further adjusted main models for two different area-levels following the European Nomenclature of Territorial Units for Statistics (NUTS); NUTS-1 (n = 3), or NUTS-3 (n = 43). RESULTS: We found no consistent differences between both exposure methods. We observed most robust associations with lung cancer mortality. Hazard Ratios (HRs) per 10 µg/m3 increase for NO2 were 1.060 (95%CI 1.042-1.078) [hybrid LUR] and 1.040 (95%CI 1.022-1.058) [RIO-IFDM]. Associations with non-accidental, respiratory disease and cardiovascular disease mortality were generally null in main models but were enhanced after further adjustment for NUTS-1 or NUTS-3. HRs for non-accidental mortality per 5 µg/m3 increase for PM2.5 for the main model using hybrid LUR exposure were 1.023 (95%CI 1.011-1.035). After including random effects HRs were 1.044 (95%CI 1.033-1.057) [NUTS-1] and 1.076 (95%CI 1.060-1.092) [NUTS-3]. CONCLUSION: Long-term air pollution exposure was associated with higher lung cancer mortality risk but not consistently with the other studied causes. Magnitude of associations varied by adjustment for geographic subdivisions, area-level socio-economic covariates and less by exposure assessment method.

Designing local air pollution policies focusing on mobility and heating to avoid a targeted number of pollution-related deaths: Forward and backward approaches combining air pollution modeling, health impact assessment and cost-benefit analysis.

CONTEXT: Policies aiming at decreasing air pollutants (e.g., fine particulate matter, PM2.5) are often designed without targeting an explicit health benefit nor carrying out cost-benefit analyses. METHODS: We developed a transdisciplinary backward and forward approach at the conurbation level: from health objectives set by local decision-makers, we estimated which reductions in PM2.5 exposures and emissions would allow to reach them, and identified urban policies leading to these reductions (backward approach). We finally conducted health impact and cost-benefit analyses of these policies (forward approach). The policies were related to the most emitting sectors in the considered area (Grenoble, France), wood heating and transport sectors. The forward approach also considered the health impact and co-benefits of these policies related to changes in physical activity and CO2 emissions. FINDINGS: Decision-makers set three health targets, corresponding to decreases by 33% to 67% in PM2.5-attributable mortality in 2030, compared to 2016. A decrease by 42% in PM2.5 exposure (from 13.9 µg/m3) was required to reach the decrease by 67% in PM2.5-attributable mortality. For each Euro invested, the total benefit was about 30€ for policies focusing on wood heating, and 1 to 68€ for traffic policies. Acting on a single sector was not enough to attain a 67% decrease in PM2.5-attributable mortality. This target could be achieved by replacing all inefficient wood heating equipment by low-emission pellet stoves and reducing by 36% the traffic of private motorized vehicles. This would require to increase the share of active modes (walking, biking…), inducing increases in physical activity and additional health benefits beyond the initial target. Annual net benefits were between €484 and €629 per capita for policies with report on active modes, compared to between €162 and €270 without. CONCLUSIONS: Urban policies strongly reducing air pollution-attributable mortality can be identified by our approach. Such policies can be cost-efficient.