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Abstract Ambient fine particulate matter (PM2.5) is the world’s leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM2.5 exposure. Here we interpret satellite-derived PM2.5 estimates over 1998-2019 and find a reversal of previous growth in global PM2.5 air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM2.5 exposure, related to both pollution levels and population size, increased from 1998 (28.3 μg/m3) to a peak in 2011 (38.9 μg/m3) and decreased steadily afterwards (34.7 μg/m3 in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM2.5-attributable mortality and increasing health benefits per µg/m3 marginal reduction in exposure, implying increasing urgency and benefits of PM2.5 mitigation with aging population and cleaner air.

Ambient air pollution in India accounts for 870 000 deaths per year, including 43 000 from road transportation. Vehicle electrification could posit a sustainable solution. However, 3/4th of India’s electric grid is powered by coal, emitting large amounts of PM _2.5 , SO _2 , and NO _x . This leads to uncertainty regarding the health benefits and distributional consequences from vehicle electrification. Our results show that if electric vehicles made up 30% of vehicle kilometers traveled, there would be 1000–2000 additional deaths each year under present day conditions. Higher increases in pollution exposure are seen in scheduled castes/tribes, poor, and rural populations particularly in high coal production states. Switching to net zero-emitting electricity generation for charging would reduce air pollution attributable deaths by 6000–7000 annually and PM _2.5 exposure across all groups of population.

Each year, millions of premature deaths worldwide are caused by exposure to outdoor air pollution, especially fine particulate matter (PM2.5). Designing policies to reduce these deaths relies on air quality modeling for estimating changes in PM2.5 concentrations from many scenarios at high spatial resolution. However, air quality modeling typically has substantial requirements for computation and expertise, which limits policy design, especially in countries where most PM2.5-related deaths occur. Lower requirement reduced-complexity models exist but are generally unavailable worldwide. Here, we adapt InMAP, a reduced-complexity model originally developed for the United States, to simulate annual-average primary and secondary PM2.5 concentrations across a global-through-urban spatial domain: “Global InMAP”. Global InMAP uses a variable resolution grid, with horizontal grid cell widths ranging from 500 km in remote locations to 4km in urban locations. We evaluate Global InMAP performance against both measurements and a state-of-the-science chemical transport model, GEOS-Chem. Against measurements, InMAP predicts total PM2.5 concentrations with a normalized mean error of 62%, compared to 41% for GEOS-Chem. For the emission scenarios considered, Global InMAP reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%–121%, which is sufficient for initial policy assessment and scoping. Global InMAP can be run on a desktop computer; simulations here took 2.6–8.4 hours. This work presents a global, open-source, reduced-complexity air quality model to facilitate policy assessment worldwide, providing a screening tool for reducing air pollution-related deaths where they occur most.

Each year, millions of premature deaths worldwide are caused by exposure to outdoor air pollution, especially fine particulate matter (PM2.5). Designing policies to reduce these deaths relies on air quality modeling for estimating changes in PM2.5 concentrations from many scenarios at high spatial resolution. However, air quality modeling typically has substantial requirements for computation and expertise, which limits policy design, especially in countries where most PM2.5-related deaths occur. Lower requirement reduced-complexity models exist but are generally unavailable worldwide. Here, we adapt InMAP, a reduced-complexity model originally developed for the United States, to simulate annual-average primary and secondary PM2.5 concentrations across a global-through-urban spatial domain: "Global InMAP". Global InMAP uses a variable resolution grid, with horizontal grid cell widths ranging from 500 km in remote locations to 4km in urban locations. We evaluate Global InMAP performance against both measurements and a state-of-the-science chemical transport model, GEOS-Chem. Against measurements, InMAP predicts total PM2.5 concentrations with a normalized mean error of 62%, compared to 41% for GEOS-Chem. For the emission scenarios considered, Global InMAP reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%-121%, which is sufficient for initial policy assessment and scoping. Global InMAP can be run on a desktop computer; simulations here took 2.6-8.4 hours. This work presents a global, open-source, reduced-complexity air quality model to facilitate policy assessment worldwide, providing a screening tool for reducing air pollution-related deaths where they occur most.

Abstract Background Some studies have linked long-term exposure to traffic related air pollutants (TRAP) with adverse cardiovascular health outcomes; however, previous studies have not linked highly variable concentrations of TRAP measured at street-level within neighborhoods to cardiovascular health outcomes. Methods Long-term pollutant concentrations for nitrogen dioxide [NO2], nitric oxide [NO], and black carbon [BC] were obtained by street-level mobile monitoring on 30 m road segments and linked to residential addresses of 41,869 adults living in Oakland during 2010 to 2015. We fit Cox proportional hazard models to estimate the relationship between air pollution exposures and time to first cardiovascular event. Secondary analyses examined effect modification by diabetes and age. Results Long-term pollutant concentrations [mean, (standard deviation; SD)] for NO2, NO and BC were 9.9 ppb (SD 3.8), 4.9 ppb (SD 3.8), and 0.36 μg/m3 (0.17) respectively. A one SD increase in NO2, NO and BC, was associated with a change in risk of a cardiovascular event of 3% (95% confidence interval [CI] -6% to 12%), 3% (95% CI -5% to 12%), and − 1% (95% CI -8% to 7%), respectively. Among the elderly (≥65 yrs), we found an increased risk of a cardiovascular event of 12% for NO2 (95% CI: 2%, 24%), 12% for NO (95% CI: 3%, 22%), and 7% for BC (95% CI: -3%, 17%) per one SD increase. We found no effect modification by diabetes. Conclusions Street-level differences in long-term exposure to TRAP were associated with higher risk of cardiovascular events among the elderly, indicating that within-neighborhood differences in TRAP are important to cardiovascular health. Associations among the general population were consistent with results found in previous studies, though not statistically significant.

An exposure-based traffic assignment (TA) model and accompanying analysis framework have been developed to quantify primary and secondary fine particulate matter (PM _2.5 ) exposure due to modeled on-road vehicle flow on a regional network at a high spatial resolution. The Chicago Metropolitan Area transportation network is used to demonstrate the model’s decision-informing power. The study compares the spatially distributed exposure impacts due to traffic emissions of two TA optimization scenarios: a baseline user equilibrium with respect to travel time (UET) and a novel system optimal with respect to pollutant intake (SOI). The UET and SOI scenarios are developed through the use of (a) the TA model used for obtaining vehicle flow patterns and characteristics including emissions, (b) a source-receptor matrix for PM _2.5 developed through a reduced-complexity air quality model to quantify primary and secondary PM _2.5 concentrations across the exposure domain, (c) spatial analysis for assessing exposure profiles at the census tract level, and (d) a health impact model to quantify exposure damages. The SOI scenario yields a 9% – 10% total reduction in exposure damages, with the most impacted census tracts benefiting from up to 20% – 30% of reductions, but leads to a 16% increase in travel time costs. Further reduction to PM _2.5 exposure by the SOI is hindered by network constraints, where travel demand in populous areas around the network must still be satisfied. The model can be used to systematically quantify the mitigation potential of different transportation exposure reduction strategies, to assess the exposure impacts of newly developed transportation infrastructure, and to address the equity implications of PM _2.5 exposure from traffic, all under realistic system behavior and bounded by actual system constraints.

There are hundreds of millions of kilometers of paved roads and many people live in proximity. Pollution from road transportation is a well-documented problem potentially leading to chronic health impacts. However, research on the raw material production, construction, operation, maintenance, and end-of-life phases of paved roads, and corresponding supply chains, is generally limited to energy consumption and greenhouse gas emissions. No previous research efforts on the life-cycle stages of pavements and road operation connect pollutant emission inventories to intake of inhaled pollutants and resulting damages to exposed populations. We have developed a first-of-its-kind model quantifying human exposure to fine particulate matter (PM _2.5 ) due to emissions from routine pavement resurfacing and vehicle operation. We utilize the Intervention Model Pollution Source-Receptor Matrix to calculate marginal changes in ground-level PM _2.5 concentrations and resulting exposure intake from a spatially resolved primary and secondary PM _2.5 emission precursors inventory. Under a scenario of annual road-resurfacing practices within the San Francisco Bay Area in California (population: 7.5 million), resurfacing activities, material production and delivery (i.e. cement, concrete, aggregate, asphalt, bitumen), and fuel (i.e. gasoline, diesel) supply chains contribute almost 65% to the annual PM _2.5 intake from all the sources included in the study domain (the remaining 35% being due to on-road tailpipe emissions). Exposure damages range from $170 to $190 million (2019 USD). Complete electrification of on-road mobile sources would reduce annual intake by 64%, but a sizable portion would remain from material supply chains, construction activities, and brake and tire wear. Future mitigation policies should be enacted equitably. Results show that people of color experience higher-than-average PM _2.5 exposure disparities from the emission sources included in the study, particularly from material production.

Long- and short-term exposure to airborne pollutants results in adverse health effects. Regulatory monitors can be used to determine if regional concentrations meet regulatory standards of air pollution. As assessments of air pollutant exposure become more spatially resolved, evaluation is needed to assess the spatial representativeness of monitors in different environments. We measured NO2, ultrafine particle concentration (UFP), and PM1 with both stationary and mobile platforms in Pittsburgh, PA in 2016 and 2017. We sampled in eight ∼1 km2 neighborhoods representing different land use and exposure regimes (e.g., urban and suburban, high and low traffic). Mobile sampling was conducted on up to 25 days in each neighborhood to study fine-scale spatial variation in pollutant concentrations. NO2 exhibited within-neighborhood spatial variation, with hotspots elevated by up to a factor of 5 above the regional background. Spatial differences in UFP within the same 1 km2 neighborhoods could be a factor of 2.4 times regional background. PM1 was more regional and less spatially variable. Most neighborhoods exhibited less than 1 μg m−3 spatial variability in PM1. Spatial variability of NO2 and UFP showed moderate correlation (R2 > 0.5) with traditional land use covariates such as traffic volume and restaurant density. We used the Wilcoxon rank-sum test to calculate the fraction of each neighborhood represented by the same underlying concentration distribution. PM1 was the most spatially homogeneous, with 80–100% of each 1 km2 area being statistically similar to a reference location. Quantifying pollutant spatial patterns with high fidelity (e.g.,