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Indonesia faces significant air quality issues due to multiple emissions sources, including rapid urbanization and peatland fires associated with agricultural land management. Limited prior research has estimated the episodic shock of intense fires on morbidity and mortality in Indonesia but has largely ignored the impact of poor air quality throughout the year on biomarkers of cardiovascular disease risk. We conducted a cross-sectional study of the association between particulate matter less than 2.5 microns in diameter (PM2.5) and blood pressure. Blood pressure measurements were obtained from the fifth wave of the Indonesian Family Life Survey (IFLS5), an ongoing population-based socioeconomic and health survey. We used the GEOS-Chem chemical transport model to simulate daily PM2.5 concentrations at 0.5° × 0.625° resolution across the IFLS domain. We assessed the association between PM2.5 and diastolic and systolic blood pressure, using mixed effects models with random intercepts for regency/municipality and household and adjusted for individual covariates. An interquartile range increase in monthly PM2.5 exposure was associated with a 0.234 (95% CI: 0.003, 0.464) higher diastolic blood pressure, with a greater association seen in participants age 65 and over (1.16 [95% CI: 0.24, 2.08]). For the same exposure metric, there was a 1.90 (95% CI: 0.43, 3.37) higher systolic blood pressure in participants 65 and older. Our assessment of fire-specific PM2.5 yielded null results, potentially due to the timing and locations of health data collection. To our knowledge, this is the first study to provide evidence for an association between PM2.5 and blood pressure in Indonesia.

The year 2020 brought unimaginable challenges in public health, with the confluence of the COVID-19 pandemic and wildfires across the western United States. Wildfires produce high levels of fine particulate matter (PM2.5). Recent studies reported that short-term exposure to PM2.5 is associated with increased risk of COVID-19 cases and deaths. We acquired and linked publicly available daily data on PM2.5, the number of COVID-19 cases and deaths, and other confounders for 92 western U.S. counties that were affected by the 2020 wildfires. We estimated the association between short-term exposure to PM2.5 during the wildfires and the epidemiological dynamics of COVID-19 cases and deaths. We adjusted for several time-varying confounding factors (e.g., weather, seasonality, long-term trends, mobility, and population size). We found strong evidence that wildfires amplified the effect of short-term exposure to PM2.5 on COVID-19 cases and deaths, although with substantial heterogeneity across counties.

Emissions of particulate matter from fires associated with land management practices in Indonesia contribute to regional air pollution and mortality. We assess the public health benefits in Indonesia, Malaysia, and Singapore from policies to reduce fires by integrating information on fire emissions, atmospheric transport patterns, and population exposure to fine particulate matter (PM2.5). We use adjoint sensitivities to relate fire emissions to PM2.5 for a range of meteorological conditions and find that a Business-As-Usual scenario of land use change leads, on average, to 36,000 excess deaths per year into the foreseeable future (the next several decades) across the region. These deaths are largely preventable with fire reduction strategies, such as blocking fires in peatlands, industrial concessions, or protected areas, which reduce the health burden by 66, 45, and 14%, respectively. The effectiveness of these different strategies in mitigating human health impacts depends on the location of fires relative to the population distribution. For example, protecting peatlands through eliminating all fires on such lands would prevent on average 24,000 excess deaths per year into the foreseeable future across the region because, in addition to storing large amounts of fuel, many peatlands are located directly upwind of densely populated areas. We also demonstrate how this framework can be used to prioritize restoration locations for the Indonesian Peatland Restoration Agency based on their ability to reduce pollution exposure and health burden. This scientific framework is publicly available through an online decision support tool that allows stakeholders to readily determine the public health benefits of different land management strategies.

We assess scientific evidence that has emerged since the U.S. Environmental Protection Agency's 2009 Endangerment Finding for six well-mixed greenhouse gases and find that this new evidence lends increased support to the conclusion that these gases pose a danger to public health and welfare. Newly available evidence about a wide range of observed and projected impacts strengthens the association between the risk of some of these impacts and anthropogenic climate change, indicates that some impacts or combinations of impacts have the potential to be more severe than previously understood, and identifies substantial risk of additional impacts through processes and pathways not considered in the Endangerment Finding.

Background: NOAA's Hazard Mapping System (HMS) smoke product comprises smoke plumes digitised from satellite imagery. Recent studies have used HMS as a proxy for surface smoke presence. Aims: We compare HMS with airport observations, air quality station measurements and model estimates of near-surface smoke. Methods: We quantify the agreement in numbers of smoke days and trends, regional discrepancies in levels of near-surface smoke fine particulate matter (PM2.5) within HMS polygons, and separation of total PM2.5 on smoke and non-smoke days across the contiguous US and Alaska from 2010 to 2021. Key results: We find large overestimates in HMS-derived smoke days and trends if we include light smoke plumes in the HMS smoke day definition. Outside the western US and Alaska, near-surface smoke PM2.5 within areas of HMS smoke plumes is low and almost indistinguishable across density categories, likely indicating frequent smoke aloft. Conclusions: Compared with airport, Environmental Protection Agency (EPA) and model-derived estimates, HMS most closely reflects surface smoke in the Pacific and Mountain regions and Alaska when smoke days are defined using only heavy plumes or both medium and heavy plumes. Implications: We recommend careful consideration of biases in the HMS smoke product for air quality and public health assessments of fires. NOAA's Hazard Mapping System (HMS) smoke product, which comprises smoke plumes digitised from satellite imagery, was used as a proxy for surface smoke in recent air quality and public health assessments of fires. We find biases in HMS relative to airport observations, ground measurements and model estimates of surface smoke. [ABSTRACT FROM AUTHOR]

Future climate change may bring local benefits or penalties to surface air pollution, resulting from changing temperature, precipitation, and transport patterns, as well as changes in climate-sensitive natural precursor emissions. Here, we estimate the climate penalties and benefits at the end of this century with regard to surface ozone and fine particulate matter (PM2:5; excluding dust and smoke) using a one-way offline coupling between a general circulation model and a global 3-D chemical-transport model. We archive meteorology for the present day (2005 to 2014) and end of this century (2090 to 2099) for seven future scenarios developed for Phase 6 of the Coupled Model Intercomparison Project. The model isolates the impact of forecasted anthropogenic precursor emission changes versus that of climate-only driven changes on surface ozone and PM2:5 for scenarios ranging from extreme mitigation to extreme warming. We then relate these changes to impacts on human mortality and crop production. We find ozone penalties over nearly all land areas with increasing warming. We find net benefits due to climate-driven changes in PM2:5 in the Northern Extratropics, but net penalties in the Tropics and Southern Hemisphere, where most population growth is forecast for the coming century. [ABSTRACT FROM AUTHOR]

Concentrations of atmospheric trace species in the United States have changed dramatically over the past several decades in response to pollution control strategies, shifts in domestic energy policy and economics, and economic development (and resulting emission changes) elsewhere in the world. Reliable projections of the future atmosphere require models to not only accurately describe current atmospheric concentrations, but to do so by representing chemical, physical and biological processes with conceptual and quantitative fidelity. Only through incorporation of the processes controlling emissions and chemical mechanisms that represent the key transformations among reactive molecules can models reliably project the impacts of future policy, energy and climate scenarios. Efforts to properly identify and implement the fundamental and controlling mechanisms in atmospheric models benefit from intensive observation periods, during which collocated measurements of diverse, speciated chemicals in both the gas and condensed phases are obtained. The Southeast Atmosphere Studies (SAS, including SENEX, SOAS, NOMADSS and SEAC4RS) conducted during the summer of 2013 provided an unprecedented opportunity for the atmospheric modeling community to come together to evaluate, diagnose and improve the representation of fundamental climate and air quality processes in models of varying temporal and spatial scales. This paper is aimed at discussing progress in evaluating, diagnosing and improving air quality and climate modeling using comparisons to SAS observations as a guide to thinking about improvements to mechanisms and parameterizations in models. The effort focused primarily on model representation of fundamental atmospheric processes that are essential to the formation of ozone, secondary organic aerosol (SOA) and other trace species in the troposphere, with the ultimate goal of understanding the radiative impacts of these species in the southeast and elsewhere. Here we address questions surrounding four key themes: gas-phase chemistry, aerosol chemistry, regional climate and chemistry interactions, and natural and anthropogenic emissions. We expect this review to serve as a guidance for future modeling efforts.

The hydroxyl radical (OH) largely controls the tropospheric self-cleansing capacity by reacting with gases harmful to the environment and human health. OH concentrations are determined locally by competing production and loss processes. Lacking strong observational constraints, models differ in how they balance these processes, such that the sign of past and future OH changes is uncertain. In a warmer climate, OH production will increase due to its water vapor dependence, partially offset by faster OH-methane loss. Weather-sensitive emissions will also likely increase, although their net impact on global mean OH depends on the balance between source (nitrogen oxides) and sink (reactive carbon) gases. Lightning activity increases OH, but its response to climate warming is of uncertain sign. To enable confident projections of OH, we recommend efforts to reduce uncertainties in kinetic reactions, in measured and modeled OH, in proxies for past OH concentrations, and in source and sink gas emissions. OH is strongly modulated by internal climate variability despite its lifetime of a few seconds at most, with implications for interpreting trends in methane. Improved kinetic constraints on key reactions would strengthen confidence in regional and global OH budgets, and in the response of OH to climate change. Future OH changes will depend on uncertain and compensating processes involving weather-sensitive chemistry and emissions, plus human choices. Technological solutions to climate change will likely impact tropospheric oxidizing capacity and merit further study prior to implementation. [ABSTRACT FROM AUTHOR]

Wildfires can have a significant impact on air quality in Australia during severe burning seasons, but incomplete knowledge of the injection heights of smoke plumes poses a challenge for quantifying smoke exposure. In this study, we use two approaches to quantify the fractions of fire emissions injected above the planetary boundary layer (PBL), and we further investigate the impact of plume injection fractions on daily mean surface concentrations of fine particulate matter (PM 2.5) from wildfire smoke in key cities over northern and southeastern Australia from 2009 to 2020. For the first method, we rely on climatological, monthly mean vertical profiles of smoke emissions from the Integrated Monitoring and Modelling System for wildland fires (IS4FIRES) together with assimilated PBL heights from NASA Modern-Era Retrospective Analysis for Research and Application (MERRA) version 2. For the second method, we develop a novel approach based on the Multi-angle Imaging SpectroRadiometer (MISR) observations and a random forest, machine learning model that allows us to directly predict the daily plume injection fractions above the PBL in each grid cell. We apply the resulting plume injection fractions quantified by the two methods to smoke PM 2.5 concentrations simulated by the Stochastic Time-Inverted Lagrangian Transport (STILT) model in target cities. We find that characterization of the plume injection heights greatly affects estimates of surface daily smoke PM 2.5 , especially during severe wildfire seasons, when intense heat from fires can loft smoke high in the troposphere. However, using climatological injection profiles cannot capture well the spatiotemporal variability in plume injection fractions, resulting in a 63 % underestimation of daily fire emission fluxes injected above the PBL in comparison with those fluxes derived from MISR injection fractions. Our random forest model successfully reproduces the daily injected fire emission fluxes against MISR observations (R2=0.88 , normalized mean bias = 10 %) and predicts that 27 % and 45 % of total fire emissions rise above the PBL in northern and southeastern Australia, respectively, from 2009 to 2020. Using the plume behavior predicted by the random forest method also leads to better model agreement with observed surface PM 2.5 in several key cities near the wildfire source regions, with smoke PM 2.5 accounting for 5 %–52 % of total PM 2.5 during fire seasons from 2009 to 2020. [ABSTRACT FROM AUTHOR]

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low‐cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low‐cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low‐cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost‐constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low‐income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low‐income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low‐cost sensors in less privileged communities. Plain Language Summary: Communities in the United States with lower socioeconomic status and larger racial minority populations more often face higher exposure to air pollution and are therefore more vulnerable to negative health effects. Government officials rely on regulatory networks and low‐cost sensors to monitor air pollution in urban areas, but these networks are typically located in wealthier or predominantly White communities. This disparity in coverage can lead to inaccuracies in exposure assessments and health impact evaluations for nonwhite and/or low‐income communities. To address this issue, we develop a data‐driven algorithm that identifies the most representative and equitable placement of fine particulate matter (PM2.5) air pollution sensors in four U.S. cities with long histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. Our algorithm places a larger number of sensors in low‐income and nonwhite neighborhoods with known environmental pollution issues, while also capturing air pollution extremes compared to networks that only consider air pollution data. Our work provides guidance for creating fair sensor networks in U.S. cities and can help increase access to air pollution data in underprivileged communities. Key Points: A method using pollution data to diagnose the optimal and equitable placement of PM2.5 sensors is presentedBy incorporating race and income information, the method shifts the sensor distribution to more nonwhite and low‐income neighborhoodsThe approach provides a roadmap for urban areas to create low‐cost sensor networks that address America's legacy of environmental racism [ABSTRACT FROM AUTHOR]

Nitrogen oxides (NOx = NO + NO2) play a critical role in regulating tropospheric chemistry, yet NOx emission estimates are subject to large uncertainties, casting doubt on our ability to accurately model secondary pollutants such as ozone. Bottom-up emissions inventories are subject to a number of uncertainties related to estimates of emission activities, scaling factors, and fuel sources. Here, we provide an additional constraint on NOx emissions and trends using nitrate wet deposition (NWD) fluxes from the United States National Atmospheric Deposition Program (NADP) and the European Monitoring and Evaluation Programme (EMEP). We use these NWD measurements to evaluate anthropogenic and total NOx trends and magnitudes in the global Community Emissions Data System (CEDS) emissions inventory and the GEOS-Chem chemical transport model from 1980–2020. Over both the United States and Europe, observed NWD trends track well with anthropogenic NOx emissions from the CEDS inventory until 2010, after which NWD trends level out in contrast to continued decreases in CEDS. After 2010, NWD trends are able to reproduce total NOx emissions trends when the influences of both anthropogenic and background sources are considered. Observed NWD fluxes are also able to capture NOx emissions decreases over the 2020 COVID-19 lockdown period and are consistent with satellite and surface measurements of NO2. These results suggest that NWD fluxes constrain total NOx emissions well. We further compare modelled and observed NWD to provide an additional line of evidence for potential overestimates of anthropogenic NOx in emissions inventories. Over the United States, we find consistent overestimates of NOx emissions in CEDS in summer from 1980–2017 averaging by 15–20 %, with overestimates most prominent in the eastern US after 2000. Over Europe, we find that NOx is overestimated in all seasons, with the strongest average overestimates occurring in summer (175 %, with a range of 50 to >500 % depending on the site) and fall (170 %, range of 39 to >500 %). These overestimates may be reduced by cutting anthropogenic NOx emissions by 50 % in CEDS over Europe (i.e., cutting the 1980–2017 average annual emissions from 8.7 to 4.3 Tg NO), but summertime and fall NOx may still need to be reduced further for observations and models to align. Overestimates may extend to other inventories such as the European Monitoring and Evaluation Programme (EMEP) inventory, which estimates comparable but lower emissions than CEDS, with a 1990–2017 average of 6.9 Tg NO relative to the CEDS 1990–2017 average of 7.8 Tg NO. We find that NOx emission reductions over Europe improve model ozone at the surface, reducing the model summertime ozone overestimate from 14 % to 2 %. [ABSTRACT FROM AUTHOR]