Concentration-response Functions Research Articles

Single- and two-pollutant Concentration-Response Functions for PM2.5 and NO2 for Quantifying Mortality Burden in Health Impact Assessments

ObjectiveHealth Impact Assessments (HIAs) for air pollutant mixtures are challenging because risk estimates are primarily derived from single-pollutant models. Combining risk estimates from multiple pollutants requires new approaches, as a simple addition of single pollutant risk estimates from correlated air pollutants may result in double counting. We investigated approaches applying concentration-response functions (CRFs) from single- and two-pollutant models in HIAs, focusing on long-term exposure to particulate matter with a diameter less than 2.5 micrometers (PM2.5) and nitrogen dioxide (NO2) and their associations with all-cause mortality. MethodsA systematic literature search of MEDLINE and EMBASE identified cohort studies employing single- and two-pollutant models of long-term exposure to PM2.5 and NO2 with all-cause mortality. Pooled CRFs were calculated through random-effects meta-analyses of risk estimates from single- and two-pollutant models. Coefficient differences were calculated by comparing single- and two-pollutant model estimates. Four approaches to estimating population-attributable fractions (PAFs) were compared: PM2.5 or NO2 single-pollutant models to represent the mixture, the sum of single-pollutant models, the sum of two-pollutant models and the sum of single-pollutant models from a larger body of evidence adjusted by coefficient difference. ResultsSeventeen papers reported both single and two-pollutant estimates. Pooled hazard ratios (HRs) for mortality from single- and two-pollutant models were 1.053 (95% confidence interval: 1.034-1.071) and 1.035 (1.014-1.057), respectively, for a 5 μg/m3 increase in PM2.5. HRs for a 10 μg/m3 increase in NO2 were 1.032 (1.014-1.049) and 1.024 (1.000-1.049) for single- and two-pollutant models, respectively. The average coefficient difference between single- and two-pollutant models was 0.017 for PM2.5 and 0.007 for NO2. Combined PAFs for the PM2.5-NO2 mixture using joint HRs from single- and two-pollutant model CRFs were 0.09 and 0.06, respectively. ConclusionUtilizing CRFs from two-pollutant models or applying the coefficient difference to a more extensive evidence base seems to mitigate the potential overestimation of mixture health impacts from adding single-pollutant CRFs.

The last decade of air pollution epidemiology and the challenges of quantitative risk assessment.

Epidemiologic research and quantitative risk assessment play a crucial role in transferring fundamental scientific knowledge to policymakers so they can take action to reduce the burden of ambient air pollution. This commentary addresses several challenges in quantitative risk assessment of air pollution that require close attention. The background to this discussion provides a summary of and conclusions from the epidemiological evidence on ambient air pollution and health outcomes accumulated since the 1990s. We focus on identifying relevant exposure-health outcome pairs, the associated concentration-response functions to be applied in a risk assessment, and several caveats in their application. We propose a structured and comprehensive framework for assessing the evidence levels associated with each exposure-health outcome pair within a health impact assessment context. Specific issues regarding the use of global or regional concentration-response functions, their shape, and the range of applicability are discussed.

Abstract 4129036: Air pollution and Cardiovascular Disease Incidence in a Pooled Analysis of 6 U.S. Cohorts

Introduction/Background: Long term air pollution is associated with cardiovascular disease (CVD) incidence, however the effects at low levels in the US are unknown. Research Questions/Hypothesis: To assess the effect of long-term exposure to ambient air pollution CVD incidence (myocardial infarction [MI], stroke, and all CVD) in a pooled analysis of epidemiological cohorts. Materials and Methods: We harmonized data from six cohorts (Cardiovascular Health Study, Nurses’ Health Study II, Multi-Ethnic Study of Atherosclerosis, and Reasons for Geographic And Racial Differences in Stroke Study, Sister Study, and Women’s Health Initiative) with both CVD incidence data and extensive covariate information. We predicted time-varying two-year average concentrations of particulate matter <2.5 microns (PM 2.5 ) and nitrogen dioxide (NO 2 ) from 1990-2019 at participant residential addresses using validated regionalized spatio-temporal models, combining spatio-temporal universal kriging and partial least squares regression with inputs from monitoring data from regulatory and researcher-deployed networks, satellite remote-sensing data and chemical transport modeling. Cox proportional hazards regression models adjusted for individual and area-level information, including smoking status, socio-economic factors, anthropometry, blood pressure, cholesterol, and other clinical variables. Results: Our analysis included over 300,000 participants and 10,229 incident CVD events. The median residential concentration of PM 2.5 was 10.1 µg/m 3 and NO 2 was 9.3 ppb. A 5 µg/m 3 increase in PM 2.5 was not significantly associated with a higher hazard ratio for all outcomes: 1.03 (95%CI: 0.96, 1.10) for CVD incidence, 1.03 (95%CI 0.93, 1.15) for MI, and 1.00 (95%CI 0.90, 1.10) for stroke. We found that a 10ppb increase in NO 2 was associated with a 1.06 (95%CI: 1.01, 1.11) higher hazard ratio for CVD, 1.07 (95%CI: 1.01, 1.11) for MI, and 1.02 (95%CI 0.96, 1.09) for stroke. Conclusions: In this analysis of a large, pooled cohort with excellent exposure, outcome, and covariate information, we found an association of NO 2 exposure—considered a surrogate for traffic-related air pollution—with CVD incidence and MI. Associations with particulate matter were not statistically significant, but could not exclude a small increased risk. We are extending this analysis to include 6 additional cohorts and an additional 700,000 participants to allow for generation of a highly resolved concentration-response function.

Effect of Biomass Burnings on Population Exposure and Health Impact at the End of 2019 Dry Season in Southeast Asia

At the end of the dry season, from early March to early April each year, extensive agricultural biomass waste burnings occur throughout insular mainland Southeast Asia. During this biomass-burning period, smoke aerosols blanketed the whole region and were transported and dispersed by predominant westerly and southwesterly winds to southern China, Taiwan, and as far southern Japan and the Philippines. The extensive and intense burnings coincided with some wildfires in the forests due to high temperatures, making the region one of the global hot spots of biomass fires. In this study, we focus on the effect of pollutants emitted from biomass burnings in March 2019 at the height of the burning period on the exposed population and their health impact. The Weather Research Forecast-Chemistry (WRF-Chem) model was used to predict the PM2.5 concentration over the simulating domain, and health impacts were then assessed on the exposed population in the four countries of Southeast Asia, namely Thailand, Laos, Cambodia, and Vietnam. Using the health impact based on log-linear concentration-response function and Integrated Exposure Response (IER), the results show that at the peak period of the burnings from 13 to 20 March 2019, Thailand experienced the highest impact, with an estimated 2170 premature deaths. Laos, Vietnam, and Cambodia followed, with estimated mortalities of 277, 565, and 315 deaths, respectively. However, when considering the impact per head of population, Laos exhibited the highest impact, followed by Thailand, Cambodia, and Vietnam. The results highlight the significant health impact of agricultural waste burnings in Southeast Asia at the end of the dry season. Hence, policymakers should take these into account to design measures to reduce the negative impact of widespread burnings on the exposed population in the region.

The impact of changing exposure to PM2.5 on mortality for US diplomats with multiple international relocations: a modelling study.

Current evidence linking long-term exposure to fine particulate matter (PM2.5) exposure and mortality is primarily based on persons that live in the same residence, city and/or country throughout the study, with few residential moves or relocations. We propose a novel method to quantify the health impacts of PM2.5 for United States (US) diplomats who regularly relocate to international cities with different PM2.5 levels. Life table methods were applied at an individual-level to US mortality statistics using the World Health Organization's database of city-specific PM2.5 annual mean concentrations. Global Burden of Disease concentration-response (C-R) functions were used to estimate cause-specific mortality and days of life lost (DLL) for a range of illustrative 20-year diplomatic assignments for three age groups. Time lags between exposure and exposure-related mortality risks were applied. Sensitivity analysis of baseline mortality, exposure level, C-R functions and lags was conducted. The effect of mitigation measures, including the addition of air purifiers, was examined. DLL due to PM2.5 exposure for a standard 20-year assignment ranged from 0.3 days for diplomats' children to 84.1 days for older diplomats. DLL decreased when assignments in high PM2.5 cities were followed by assignments in low PM2.5 cities: 162.5 DLL when spending 20 years in high PM2.5 cities compared to 62.6 DLL when spending one of every four years (5 years total) in a high PM2.5 city for older male diplomats. Use of air purifiers and improved home tightness in polluted cities may halve DLL due to PM2.5 exposure. The results were highly sensitive to lag assumptions: DLL increased by 68% without inception lags and decreased by 59% without cessation lags for older male diplomats. We developed a model to quantify health impacts of changing PM2.5 exposure for a population with frequent relocations. Our model suggests that alternating assignments in high and low PM2.5 cities may help reduce PM2.5-related mortality burdens. Adding exposure mitigation at home may help reduce PM2.5 related mortality. Further research on outcome-specific lag structures is needed to improve the model.

Spatiotemporally Detailed Quantification of Air Quality Benefits of Emissions-Part II: Sensitivity to Study Parameters and Assumptions.

Adjoint modeling, using U.S. EPA's Community Multiscale Air Quality (CMAQ), has been performed to provide location-specific monetized health benefits from the controls of primary PM2.5 and PM2.5 precursors (NO x , SO2, and NH3) across North America. Source-to-health benefit relationships are quantified using a benefit-per-ton (BPT) metric, accounting for the impacts on premature mortality due to long-term exposure to fine particulate matter. In the base analysis, the approach used a 12 km resolution, four 2-week episodes chosen to capture annual responses, emissions for 2016, and the Global Exposure Mortality Model (GEMM) to link exposures to premature mortality. Here, we investigate the impacts those choices have on results using a range of sensitivity analyses. The choice of four representative episodes led to relatively little bias and error. Finer model resolution, investigated by comparing 36, 12, 4, and 1 km simulations over two urban areas, tended to increase BPT estimates, though the impact was inconsistent between different regions. While BPTs and burden estimates were consistent across resolutions over New York City, they sharply increased for Los Angeles, particularly for NOx and ammonia, leading to 90% increase in burden estimates at 1 km resolution. We find that, for primary PM2.5 emissions, better resolved population distribution is the main contributing factor to higher BPTs, but for secondary precursor emissions (ammonia and NOx), higher model resolution that avoids dilution in coarser grids is more important. Changing emissions from 2016 to 2001 and 2028 resulted in fairly consistent primary PM2.5 BPTs but impacted the BPTs for NOx and ammonia more significantly due to changes in SO2 emissions. We found that BPTs tend to stabilize, as emission changes in 2028 lead to a lower deviation from 2016 BPTs compared to changes from the 2001 episode. The role of the epidemiological model also led to relatively modest uncertainties, 15-30% depending on the species, even when different shapes of concentration-response functions were employed. We found the impact of the choice of CRF to be larger or comparable in size to the reported epidemiological model uncertainties for log-linear CRFs. The adjoining approach proved robust to modeling choices in providing BPT estimates that are highly granular across locations and emitted species.

Identifying Key Sources for Air Pollution and CO2 Emission Co-control in China.

China is confronting the dual challenges of air pollution and climate change, mandating the co-control of air pollutants and CO2 emissions from their shared sources. Here we identify key sources for co-control that prioritize the mitigation of PM2.5-related health burdens, given the homogeneous impacts of CO2 emissions from various sources. By applying an integrated analysis framework that consists of a detailed emission inventory, a chemical transport model, a multisource fused dataset, and epidemiological concentration-response functions, we systematically evaluate the contribution of emissions from 390 sources (30 provinces and 13 socioeconomic sectors) to PM2.5-related health impacts and CO2 emissions, as well as the marginal health benefits of CO2 abatement across China. The estimated source-specific contributions exhibit substantial disparities, with the marginal benefits varying by 3 orders of magnitude. The rural residential, transportation, metal, and power and heating sectors emerge as pivotal sources for co-control, with regard to their relatively large marginal benefits or the sectoral total benefits. In addition, populous and heavily industrialized provinces such as Shandong and Henan are identified as the key regions for co-control. Our study highlights the significance of incorporating health benefits into formulating air pollution and carbon co-control strategies for improving the overall social welfare.

Choices of morbidity outcomes and concentration-response functions for health risk assessment of long-term exposure to air pollution.

Air pollution health risk assessment (HRA) has been typically conducted for all causes and cause-specific mortality based on concentration-response functions (CRFs) from meta-analyses that synthesize the evidence on air pollution health effects. There is a need for a similar systematic approach for HRA for morbidity outcomes, which have often been omitted from HRA of air pollution, thus underestimating the full air pollution burden. We aimed to compile from the existing systematic reviews and meta-analyses CRFs for the incidence of several diseases that could be applied in HRA. To achieve this goal, we have developed a comprehensive strategy for the appraisal of the systematic reviews and meta-analyses that examine the relationship between long-term exposure to particulate matter with an aerodynamic diameter smaller than 2.5 µm (PM2.5), nitrogen dioxide (NO2), or ozone (O3) and incidence of various diseases. To establish the basis for our evaluation, we considered the causality determinations provided by the US Environmental Protection Agency Integrated Science Assessment for PM2.5, NO2, and O3. We developed a list of pollutant/outcome pairs based on these assessments and the evidence of a causal relationship between air pollutants and specific health outcomes. We conducted a comprehensive literature search using two databases and identified 75 relevant systematic reviews and meta-analyses for PM2.5 and NO2. We found no relevant reviews for long-term exposure to ozone. We evaluated the reliability of these studies using an adaptation of the AMSTAR 2 tool, which assesses various characteristics of the reviews, such as literature search, data extraction, statistical analysis, and bias evaluation. The tool's adaptation focused on issues relevant to studies on the health effects of air pollution. Based on our assessment, we selected reviews that could be credible sources of CRF for HRA. We also assessed the confidence in the findings of the selected systematic reviews and meta-analyses as the sources of CRF for HRA. We developed specific criteria for the evaluation, considering factors such as the number of included studies, their geographical distribution, heterogeneity of study results, the statistical significance and precision of the pooled risk estimate in the meta-analysis, and consistency with more recent studies. Based on our assessment, we classified the outcomes into three lists: list A (a reliable quantification of health effects is possible in an HRA), list B+ (HRA is possible, but there is greater uncertainty around the reliability of the CRF compared to those included on list A), and list B- (HRA is not recommended because of the substantial uncertainty of the CRF). In our final evaluation, list A includes six CRFs for PM2.5 (asthma in children, chronic obstructive pulmonary disease, ischemic heart disease events, stroke, hypertension, and lung cancer) and three outcomes for NO2 (asthma in children and in adults, and acute lower respiratory infections in children). Three additional outcomes (diabetes, dementia, and autism spectrum disorders) for PM2.5 were included in list B+. Recommended CRFs are related to the incidence (onset) of the diseases. The International Classification of Diseases, 10th revision codes, age ranges, and suggested concentration ranges are also specified to ensure consistency and applicability in an HRA. No specific suggestions were given for ozone because of the lack of relevant systematic reviews. The suggestions formulated in this study, including CRFs selected from the available systematic reviews, can assist in conducting reliable HRAs and contribute to evidence-based decision-making in public health and environmental policy. Future research should continue to update and refine these suggestions as new evidence becomes available and methodologies evolve.

Associations between long-term ambient PM2.5 exposure and the incidence of cardiopulmonary diseases and diabetes, attributable years lived with disability, and policy implication

Long-term exposure to ambient PM2.5 is known associated with cardiovascular and respiratory health effects. However, the heterogeneous concentrationresponse function (CRF) between PM2.5 exposure across different concentration range and cardiopulmonary disease and diabetes mellitus (DM) incidence, and their implications on attributable years lived with disability (YLD) and regulation policy has not been well-studied. In this retrospective longitudinal cohort study, disease-free participants (approximately 170,000 individuals, aged ≥ 30 years) from the MJ Health Database were followed up (2007–2017) regarding incidents of coronary heart disease (CHD), ischemic stroke, chronic obstructive pulmonary disease (COPD), lower respiratory tract infections (LRIs), and DM. We used a time-dependent nonlinear weight-transformation Cox regression model for the CRF with an address-matched 3-year mean PM2.5 exposure estimate. Town/district-specific PM2.5-attributable YLD were calculated by multiplying the disease incidence rate, population attributable fraction, disability weight, and sex-age group specific subpopulation for each disease separately. The estimated CRFs for cardiopulmonary diseases were heterogeneously with the hazard ratios (HRs) increased rapidly for CHD and ischemic stroke at PM2.5 concentration lower than 10 μg/m3, whereas the HRs for DM (LRIs) increased with PM2.5 higher than 15 (20) μg/m3. Women had higher HRs for ischemic stroke and DM but not CHD. Relative to the lowest observed PM2.5 concentration of 6 μg/m3 of the study population, the PM2.5 level with an extra risk of 0.1 % (comparable to the disease incidence) for CHD, ischemic stroke, DM, and LRIs were 8.59, 11.85, 22.09, and 24.23 μg/m3, respectively. The associated attributable YLD decreased by 51.4 % with LRIs reduced most (83.6 %), followed by DM (63.7 %) as a result of PM2.5 concentration reduction from 26.10 to 16.82 μg/m3 during 2011–2019 in Taiwan. The proportion of YLD due to CHD and ischemic stroke remained dominant (56.4 %-69.9 %). The cost-benefit analysis for the tradeoff between avoidable YLD and mitigation cost suggested an optimal PM2.5 exposure level at 12 μg/m3. CRFs for cardiopulmonary diseases, attributable YLD, and regulation level, may vary depending on the national/regional background and spatial distribution of PM2.5 concentrations, as well as demographic characteristics.

Disparities in air pollution attributable mortality in the US population by race/ethnicity and sociodemographic factors.

There are large differences in premature mortality in the USA by race/ethnicity, education, rurality and social vulnerability index groups. Using existing concentration-response functions, published particulate matter (PM2.5) air pollution estimates, population estimates at the census tract level and county-level mortality data from the US National Vital Statistics System, we estimated the degree to which these mortality discrepancies can be attributed to differences in exposure and susceptibility to PM2.5. We show that differences in PM2.5-attributable mortality were consistently more pronounced by race/ethnicity than by education, rurality or social vulnerability index, with the Black American population having the highest proportion of deaths attributable to PM2.5 in all years from 1990 to 2016. Our model estimates that over half of the difference in age-adjusted all-cause mortality between the Black American and non-Hispanic white population was attributable to PM2.5 in the years 2000 to 2011. This difference decreased only marginally between 2000 and 2015, from 53.4% (95% confidence interval 51.2-55.9%) to 49.9% (95% confidence interval 47.8-52.2%), respectively. Our findings underscore the need for targeted air quality interventions to address environmental health disparities.

Two Decades of Air Pollution Health Risk Assessment: Insights From the Use of WHO's AirQ and AirQ+ Tools.

We evaluated studies that used the World Health Organization's (WHO) AirQ and AirQ+ tools for air pollution (AP) health risk assessment (HRA) and provided best practice suggestions for future assessments. We performed a comprehensive review of studies using WHO's AirQ and AirQ+ tools, searching several databases for relevant articles, reports, and theses from inception to Dec 31, 2022. We identified 286 studies that met our criteria. The studies were conducted in 69 countries, with most (57%) in Iran, followed by Italy and India (∼8% each). We found that many studies inadequately report air pollution exposure data, its quality, and validity. The decisions concerning the analysed population size, health outcomes of interest, baseline incidence, concentration-response functions, relative risk values, and counterfactual values are often not justified, sufficiently. Many studies lack an uncertainty assessment. Our review found a number of common shortcomings in the published assessments. We suggest better practices and urge future studies to focus on the quality of input data, its reporting, and associated uncertainties.

Mortality attributable to PM 2.5 from wildland fires in California from 2008 to 2018

In California, wildfire risk and severity have grown substantially in the last several decades. Research has characterized extensive adverse health impacts from exposure to wildfire-attributable fine particulate matter (PM 2.5 ), but few studies have quantified long-term outcomes, and none have used a wildfire-specific chronic dose-response mortality coefficient. Here, we quantified the mortality burden for PM 2.5 exposure from California fires from 2008 to 2018 using Community Multiscale Air Quality modeling system wildland fire PM 2.5 estimates. We used a concentration-response function for PM 2.5 , applying ZIP code–level mortality data and an estimated wildfire-specific dose-response coefficient accounting for the likely toxicity of wildfire smoke. We estimate a total of 52,480 to 55,710 premature deaths are attributable to wildland fire PM 2.5 over the 11-year period with respect to two exposure scenarios, equating to an economic impact of $432 to $456 billion. These findings extend evidence on climate-related health impacts, suggesting that wildfires account for a greater mortality and economic burden than indicated by earlier studies.

Sub-grid Variability and its Impact on Exposure in Regional Scale Air Quality and Integrated Assessment Models: Application of the uEMEP Downscaling Model

Regional scale air quality and integrated assessment models are necessarily limited in their spatial resolution, particularly when applied to larger regions such as Europe or for global applications. The EMEP MSC-W chemical transport model and the GAINS integrated assessment model, which makes use of source receptor information precalculated by the EMEP MSC-W model, use resolutions of 0.1°×0.1° and 0.3°×0.2°. These resolutions cannot account for variability at finer scales. Variability within grids, both concentration and population distributions, can be significant. To improve exposure calculations that take into account the sub-grid variability, the uEMEP model has been applied in this study to provide suitable parameterisations of the sub-grid variability within an EMEP MSC-W grid. The uEMEP model is applied on a European domain at 250 × 250 m2. This provides roughly 7000 sub-grids within each EMEP MSC-W 0.3°×0.2° grid to analyse the sub-grid variability in exposure. The analysis derives an exposure correction factor (ECF) for each source sector in each EMEP MSC-W grid. This factor provides information on the impact of sub-grid resolution on the population weighted concentrations within each grid. The population weighted standard deviation is also calculated, providing information on sub-grid variability to derive frequency distributions and parameterised forms of the sub-grid variability suitable for health risk assessments. The impact of sub-grid variability is assessed for the pollutants PM2.5, NOX and NO2. For NO2, additional chemistry calculations are carried out on the NOX frequency distributions. We show that the parameterised form of the sub-grid variability provides close to equivalent results as the original uEMEP calculations. Exposure to PM2.5 increases by around 13% when the sub-grid variability is taken into account for a 2030 emission scenario. For NO2, this increase is around 31% for the same scenario. A similar increase is found in the health risk indicator ’population attributable fraction’ when no lower concentration threshold is used in the concentration–response function. When applying the World Health Organizations recommended threshold value we find that including the sub-grid variability increases the calculated population attributable fraction by 32% and 46% for PM2.5 and NO2 respectively. We conclude that sub-grid variability should be taken into account in future exposure calculations for regional scale modelling and impact assessments.

Air pollution mortality from India’s coal power plants: unit-level estimates for targeted policy

Air pollution from coal-fired electricity generation is an important cause of premature mortality in India. Although pollution-related mortality from the sector has been extensively studied, the relative contribution of individual coal-fired units to the fleet-wide mortality burden remains unclear. Here, we find that emissions from a small number of units drive overall mortality. Units producing just 3.5% of total generation and constituting less than 3% of total capacity result in 25% of annual premature mortality from coal-fired generation. This is a direct consequence of the 200-fold variation that we find in the mortality intensity of electricity generation across units. We use a detailed emissions inventory, a reduced complexity air quality model, and non-linear PM2.5 concentration-response functions to estimate marginal premature mortality for over 500 units operational in 2019. Absolute annual mortality ranges from less than 1 to over 650 deaths/year across units, and the mortality intensity of generation varies from under 0.002 to 0.43 deaths/GWh. Our findings suggest the potential for large social benefits in the form of reduced PM2.5-related premature mortality in India if the highest mortality intensity units are prioritized for the implementation of pollution control technologies or accelerated retirement.

Projecting non-communicable diseases attributable to air pollution in the climate change era: a systematic review

ObjectivesClimate change is a major global issue with significant consequences, including effects on air quality and human well-being. This review investigated the projection of non-communicable diseases (NCDs) attributable to air...

Health benefits to the pediatric population from reduction of ambient particulate matter in Krakow, Poland

There have been few assessments of the health benefits to children of policies aimed at curbing fossil fuel-related air pollution. This has resulted in a lack of awareness regarding their positive impact on the health of this vulnerable population. We estimated the pediatric health benefits of policies targeting coal burning in one of Europe’s most polluted cities, Kraków, Poland. We combined available data on child health outcomes, related concentration-response functions, childhood population counts, and concentrations of PM2.5 and PM10 based on city-wide air monitoring in Kraków. Two exposure reduction scenarios were examined. First, we used the observed decrease in air pollutant concentrations between 2010 and 2019. Second, we hypothesized a reduction to the annual World Health Organization (WHO) air quality guideline values issued in 2005. Between 2010 and 2019, the mean annual air pollution levels in Kraków decreased for both PM2.5 and PM10. Annual average PM2.5 concentrations declined by 39.1%, reaching 23.3 μg m−3; PM10 dropped by 39.2% to 34.6 μg m−3. These reductions in air pollution can be linked to numerous actions undertaken at local and national levels. We estimate that the forgone benefits in 2010 from not having achieved the PM levels observed in 2019 (on an annual basis) included: 505 (35.7%) fewer incident cases of asthma in the 1–14 age group, 81 fewer preterm births (16.8% decrease), 52 fewer cases of low birth weight (12.3% decrease), and 59 avoided asthma hospitalizations in 0–18 year olds. Compliance with the 2005 WHO PM2.5 guidelines in 2010 would have avoided 780 incident asthma cases in the 1–14 age group (54.5% decrease), 138 preterm births (28.3% decrease), and 90 cases of low birth weight (21.2% reduction) and 219 (54.2%) fewer asthma hospitalizations in 0–18 year olds. Large health benefits were also estimated for PM10 in both scenarios. This study estimated substantial health benefits for children in Kraków, which were largely attributable to clean air policies that restrict the use of coal and other solid fuels. Kraków provides a model for other cities in Europe and beyond that are affected by coal pollution and have high rates of preterm birth, low birth weight, and respiratory illness.

Significant human health co-benefits of mitigating African emissions.

Future African aerosol emissions, and therefore air pollution levels and health outcomes, are uncertain and understudied. Understanding the future health impacts of pollutant emissions from this region is crucial. Here, this research gap is addressed by studying the range in the future health impacts of aerosol emissions from Africa in the Shared Socioeconomic Pathway (SSP) scenarios, using the UK Earth System Model version 1 (UKESM1), along with human health concentration-response functions. The effects of Africa following a high-pollution aerosol pathway are studied relative to a low-pollution control, with experiments varying aerosol emissions from industry and biomass burning. Using present-day demographics, annual deaths within Africa attributable to ambient particulate matter are estimated to be lower by 150 000 (5th-95th confidence interval of 67 000-234 000) under stronger African aerosol mitigation by 2090, while those attributable to O3 are lower by 15 000 (5th-95th confidence interval of 9000-21 000). The particulate matter health benefits are realised predominantly within Africa, with the O3-driven benefits being more widespread - though still concentrated in Africa - due to the longer atmospheric lifetime of O3. These results demonstrate the important health co-benefits from future emission mitigation in Africa.

Evaluating the sensitivity of mortality attributable to pollution to modeling Choices: A case study for Colorado

We evaluated the sensitivity of estimated PM2.5 and NO2 health impacts to varying key input parameters and assumptions including: 1) the spatial scale at which impacts are estimated, 2) using either a single concentration–response function (CRF) or using racial/ethnic group specific CRFs from the same epidemiologic study, 3) assigning exposure to residents based on home, instead of home and work locations for the state of Colorado. We found that the spatial scale of the analysis influences the magnitude of NO2, but not PM2.5, attributable deaths. Using county-level predictions instead of 1 km2 predictions of NO2 resulted in a lower estimate of mortality attributable to NO2 by ∼ 50 % for all of Colorado for each year between 2000 and 2020. Using an all-population CRF instead of racial/ethnic group specific CRFs results in a 130 % higher estimate of annual mortality attributable for the white population and a 40 % and 80 % lower estimate of mortality attributable to PM2.5 for Black and Hispanic residents, respectively. Using racial/ethnic group specific CRFs did not result in a different estimation of NO2 attributable mortality for white residents, but led to ∼ 50 % lower estimates of mortality for Black residents, and 290 % lower estimate for Hispanic residents. Using NO2 based on home instead of home and workplace locations results in a smaller estimate of annual mortality attributable to NO2 for all of Colorado by 2 % each year and 0.3 % for PM2.5. Our results should be interpreted as an exercise to make methodological recommendations for future health impact assessments of pollution.

Long-Term Exposure to Particulate Matter and Mortality: An Update of the WHO Global Air Quality Guidelines Systematic Review and Meta-Analysis.

For the development of the 2021 global air quality guidelines, the World Health Organization (WHO) commissioned a series of systematic reviews and meta-analyses to assess the association between exposure to air pollution and all-cause and cause-specific mortality. One of these reviews, which we aim to update, focused on the effects of long-term exposure to PM2.5 and PM10 on all-cause and cause-specific mortality. The protocol for this study was registered in PROSPERO (CRD42023425327). We searched the PubMed and Embase databases for studies published between September 2018 and May 2023. Study-specific effects were pooled using random-effects models. We included 106 studies in the meta-analysis, 46 studies from the previous review and 60 from this update. All exposure-outcome pairs analysed showed positive and significant associations, except for PM10 and cerebrovascular mortality. The certainty of the evidence was rated as high for the majority of exposure-outcome pairs. We included a large number of new cohorts, and provided new concentration-response functions that will inform WHO advice on the use of this information for air pollution health risk assessments.

Mortality burden attributable to exceptional PM2.5 air pollution events in Australian cities: A health impact assessment

BackgroundPeople living in Australian cities face increased mortality risks from exposure to extreme air pollution events due to bushfires and dust storms. However, the burden of mortality attributable to exceptional PM2.5 levels has not been well characterised. We assessed the burden of mortality due to PM2.5 pollution events in Australian capital cities between 2001 and 2020. MethodsFor this health impact assessment, we obtained data on daily counts of deaths for all non-accidental causes and ages from the Australian National Vital Statistics Register. Daily concentrations of PM2.5 were estimated at a 5 km grid cell, using a Random Forest statistical model of data from air pollution monitoring sites combined with a range of satellite and land use-related data. We calculated the exceptional PM2.5 levels for each extreme pollution exposure day using the deviation from a seasonal and trend loess decomposition model. The burden of mortality was examined using a relative risk concentration-response function suggested in the literature. FindingsOver the 20-year study period, we estimated 1454 (95 % CI 987, 1920) deaths in the major Australian cities attributable to exceptional PM2.5 exposure levels. The mortality burden due to PM2.5 exposure on extreme pollution days was considerable. Variations were observed across Australia. Despite relatively low daily PM2.5 levels compared to global averages, all Australian cities have extreme pollution exposure days, with PM2.5 concentrations exceeding the World Health Organisation Air Quality Guideline standard for 24-h exposure. Our analysis results indicate that nearly one-third of deaths from extreme air pollution exposure can be prevented with a 5 % reduction in PM2.5 levels on days with exceptional pollution. InterpretationExposure to exceptional PM2.5 events was associated with an increased mortality burden in Australia's cities. Policies and coordinated action are needed to manage the health risks of extreme air pollution events due to bushfires and dust storms under climate change.