Source-Receptor Relationships Between Precursor Emissions and O3 and PM2.5 Air Pollution Impacts.

Urban residential energy switching in China between 1980 and 2014 prevents 2.2 million premature deaths

Abstract. Photochemical grid models (PGMs) are used to simulate tropospheric ozone and quantify its response to emission changes. PGMs are often applied for annual simulations to provide both maximum concentrations for assessing compliance with air quality standards and frequency distributions for assessing human exposure. Efficient methods for computing ozone at different emission levels can improve the quality of ozone air quality management efforts. This study demonstrates the feasibility of using the decoupled direct method (DDM) to calculate first- and second-order sensitivity of ozone to anthropogenic NOx and VOC emissions in annual PGM simulations at continental scale. Algebraic models are developed that use Taylor series to produce complete annual frequency distributions of hourly ozone at any location and any anthropogenic emission level between zero and 100%, adjusted independently for NOx and VOC. We recommend computing the sensitivity coefficients at the midpoint of the emissions range over which they are intended to be applied, in this case with 50% anthropogenic emissions. The algebraic model predictions can be improved by combining sensitivity coefficients computed at 10 and 50% anthropogenic emissions. Compared to brute force simulations, algebraic model predictions tend to be more accurate in summer than winter, at rural than urban locations, and with 100% than zero anthropogenic emissions. Equations developed to combine sensitivity coefficients computed with 10 and 50% anthropogenic emissions are able to reproduce brute force simulation results with zero and 100% anthropogenic emissions with a mean bias of less than 2 ppb and mean error of less than 3 ppb averaged over 22 US cities.

Abstract. Increases in surface ozone (O3) and fine particulate matter (≤2.5 μm aerodynamic diameter, PM2.5) are associated with excess premature human mortalities. We estimate changes in surface O3 and PM2.5 from pre-industrial (1860) to present (2000) and the global present-day (2000) premature human mortalities associated with these changes. We extend previous work to differentiate the contribution of changes in three factors: emissions of short-lived air pollutants, climate change, and increased methane (CH4) concentrations, to air pollution levels and associated premature mortalities. We use a coupled chemistry-climate model in conjunction with global population distributions in 2000 to estimate exposure attributable to concentration changes since 1860 from each factor. Attributable mortalities are estimated using health impact functions of long-term relative risk estimates for O3 and PM2.5 from the epidemiology literature. We find global mean surface PM2.5 and health-relevant O3 (defined as the maximum 6-month mean of 1-h daily maximum O3 in a year) have increased by 8 ± 0.16 μg m−3 and 30 ± 0.16 ppbv (results reported as annual average ±standard deviation of 10-yr model simulations), respectively, over this industrial period as a result of combined changes in emissions of air pollutants (EMIS), climate (CLIM) and CH4 concentrations (TCH4). EMIS, CLIM and TCH4 cause global population-weighted average PM2.5 (O3) to change by +7.5 ± 0.19 μg m−3 (+25 ± 0.30 ppbv), +0.4 ± 0.17 μg m−3 (+0.5 ± 0.28 ppbv), and 0.04 ± 0.24 μg m−3 (+4.3 ± 0.33 ppbv), respectively. Total global changes in PM2.5 are associated with 1.5 (95% confidence interval, CI, 1.2–1.8) million cardiopulmonary mortalities and 95 (95% CI, 44–144) thousand lung cancer mortalities annually and changes in O3 are associated with 375 (95% CI, 129–592) thousand respiratory mortalities annually. Most air pollution mortality is driven by changes in emissions of short-lived air pollutants and their precursors (95% and 85% of mortalities from PM2.5 and O3 respectively). However, changing climate and increasing CH4 concentrations also contribute to premature mortality associated with air pollution globally (by up to 5% and 15%, respectively). In some regions, the contribution of climate change and increased CH4 together are responsible for more than 20% of the respiratory mortality associated with O3 exposure. We find the interaction between climate change and atmospheric chemistry has influenced atmospheric composition and human mortality associated with industrial air pollution. Our study highlights the benefits to air quality and human health of CH4 mitigation as a component of future air pollution control policy.

Conditional probability functions are commonly used for source identification purposes in air pollution studies. CBPF (conditional bivariate probability function) categorizes the probability of high concentrations being observed at a location by wind direction/speed and investigate the directionality of local sources. PSCF (potential source contribution function), a trajectory-ensemble method, identifies the source regions most likely to be associated with high measured concentrations. However, these techniques do not allow the direct identification of areas where changes in emissions have occurred. This study presents an extension of conditional probability methods in which the differences between conditional probability values for temporally different sets of data can be used to explore changes in emissions from source locations. The differential CBPF and differential PSCF were tested using a long-term series of air quality data (12 years; 2005/2016) collected in Rochester, NY. The probability functions were computed for each of 4 periods that represent known changes in emissions. Correlation analyses were also performed on the results to find pollutants undergoing similar changes in local and regional sources. The differential probability functions permitted the identification of major changes in local and regional emission location. In Rochester, changes in local air pollution were related to the shutdown of a large coal power plant (SO2) and to the abatement measures applied to road and off-road traffic (primary pollutants). The concurrent effects of these changes in local emissions were also linked to reduced concentrations of nucleation mode particles. Changes in regional source areas were related to the decreases in secondary inorganic aerosol and organic carbon. The differential probabilities for sulfate, nitrate, and organic aerosol were consistent with differences in the available National Emission Inventory annual emission values. Changes in the source areas of black carbon and PM2.5 mass concentrations were highly correlated.

Exposure to outdoor air pollution is the largest environmental risk factor for death and disease worldwide, associated with millions of cases of excess deaths (mortality) each year. Although there are many pollutants in the air that affect our health, the most important class of pollutants is fine particulate matter, PM2.5, which are airborne particles of diameter =2.5 micrometers (µm). These particles are small enough to deposit deep in the respiratory system where they can then enter the bloodstream, traveling and causing damage to other bodily systems. Exposure to outdoor (ambient) PM2.5 has been found to be the most important environmental risk factor for mortality in Southeast Asia, associated with 130,000 - 320,000 excess deaths in Association of Southeast Asian Nations (ASEAN) member countries in 2019. Southeast Asia, especially its mega-cities, but also other areas, has some of the worst air quality in the world. Almost all human activity emits air pollutants. Fine particulate matter is both directly emitted and formed in the atmosphere through chemical reactions, the latter of which requires modeling to predict. Power generation is one of the major sources of air pollutants that lead to elevated concentrations of fine particulate matter, including in Southeast Asia. Fossil fuel combustion for power generation, especially coal but also diesel, is the main source of air pollutant emissions from power generation. While natural gas burns cleaner than coal or diesel, in the quantities combusted for power generation in Southeast Asia, it is also a significant emitter. This study augments the ASEAN Interconnection Masterplan Study III (AIMS III) by quantifying changes to air quality and human health that result from its renewable integration and transmission interconnection scenarios. Performing this analysis requires translation of the changes in projected generation from different power sector fuel sources in the AIMS III scenarios to changes in air pollutant emissions, developing what's known as an emissions inventory for each scenario and year evaluated. We then use for the first time a new global, reduced-complexity air quality model to transform the changes in emissions to changes in air pollutant concentration of the deadliest air pollutant for human health, fine particulate matter (or PM2.5). The air quality model, Global InMAP, then utilizes the location of human population in ASEAN countries to calculate exposure to PM2.5 concentration changes and translates that to estimates of excess mortality attributable to the AIMS III scenarios.

Long-Range Transport and Deposition of Air Pollution

This study investigates long-term (i.e., 2007–2014) fluctuations in ambient ozone formation regimes for cities adjacent to shale plays in the Northeast United States (U.S.). Ozone air quality in many cities of the Northeast U.S. does not meet the U.S. National Ambient Air Quality Standards (NAAQS), and understanding ambient ozone formation regimes is essential to develop effective air pollution mitigation strategies for cities violating the air quality standards. Since 2013, the U.S. has become the world’s largest producer of tight oil and natural gas from shale rock, and previous studies show that emissions of air pollutant precursors from shale oil and gas-related activities would have the potential to affect ambient ozone air quality in adjacent cities of shale plays. This work leveraged (1) satellite-retrieved column densities of formaldehyde (HCHO) and nitrogen dioxide (NO2) from multiple instruments (i.e., Ozone Monitoring Instrument (OMI) and Global Ozone Monitoring Experiment-2 (GOME-2)); (2) photochemical air quality modeling and sensitivity analysis; and (3) ratios of satellite-retrieved air pollutant column densities to investigate ambient ozone formation regimes in neighboring cities of shale plays (i.e., Marcellus Shale) in the Northeast U.S. from 2007 to 2014. Our results show that ambient ozone formation in Boston, Pittsburgh, Philadelphia and Washington, D.C. (which are close to Marcellus Shale) was in the NOx -limited or transition regime during the period of study. Ambient ozone formation in New York City was in the transition regime during 2010–2013 and VOC -limited regime during 2007–2009 and in 2014. Based on the result of this study, we conclude that controls NOx emissions would mitigate ozone air pollution from 2007 to 2014 in most of the cities examined in this study. Controls of local VOC emissions would ease ozone air pollution in New York City during the study period. With projected increases in oil and gas production from shale plays in the Northeast U.S., air pollutant emissions from oil and gas-related activities are expected to increase in the future. The results of this study imply that controls of ozone precursor emissions from shale oil and gas-related activities could be a potential strategy for reducing ambient ozone formation in cities adjacent to the shale plays in Northeast U.S. in the future.

Air pollution and respiratory health-Do we really need more evidence?

There is limited suggestive evidence of relationships between public transport strikes and either increased air pollution or worse population health. In this study we aimed to assess whether public transport strikes were associated with increases in health events (overall, cardiovascular and respiratory mortality, and cardiovascular and respiratory hospitalizations). We also explored whether air pollution mediated those associations. We used data from the city of Barcelona (Spain) for the period 2005-2016 on strikes, health events, and ambient air pollution (nitrogen dioxide, nitrogen monoxide, particulate matter (PM) with an aerodynamic diameter ≤10 μm, PM with an aerodynamic diameter ≤2.5μm, PM with an aerodynamic diameter ≤1μm, number of particles with a diameter greater than 5 nm per cm3 (particle number concentration), and black carbon). We used linear and quasi-Poisson regression models to explore the associations between air pollution and public transport strikes and between public transport strikes and health outcomes. We also investigated potential causal mediation by air pollution. Overall, this study suggested that public transport strikes are associated with increased overall mortality, respiratory mortality, and respiratory hospitalizations. However, our findings suggest that such increases are not mediated by the increase in air pollution. Our results indicate the need to further investigate these relationships and potential mechanisms.

Mitigating air pollution: planetary health awaits a cosmopolitan moment

Air quality accountability: Developing long-term daily time series of pollutant changes and uncertainties in Atlanta, Georgia resulting from the 1990 Clean Air Act Amendments

Despite growing awareness of the mental health damage caused by air pollution, the epidemiologic evidence on impact of air pollutants on major mental disorders (MDs) remains limited. We aim to explore the impact of various air pollutants on the risk of major MD. This prospective study analyzed data from 170 369 participants without depression, anxiety, bipolar disorder, and schizophrenia at baseline. The concentrations of particulate matter with aerodynamic diameter≤2.5μm (PM2.5), particulate matter with aerodynamic diameter>2.5μm, and≤10μm (PM2.5-10), nitrogen dioxide (NO2), and nitric oxide (NO) were estimated using land-use regression models. The association between air pollutants and incident MD was investigated by Cox proportional hazard model. During a median follow-up of 10.6years, 9 004 participants developed MD. Exposure to air pollution in the highest quartile significantly increased the risk of MD compared with the lowest quartile: PM2.5 (hazard ratio [HR]: 1.16, 95% CI: 1.09-1.23), NO2 (HR: 1.12, 95% CI: 1.05-1.19), and NO (HR: 1.10, 95% CI: 1.03-1.17). Subgroup analysis showed that participants with lower income were more likely to experience MD when exposed to air pollution. We also observed joint effects of socioeconomic status or genetic risk with air pollution on the MD risk. For instance, the HR of individuals with the highest genetic risk and highest quartiles of PM2.5 was 1.63 (95% CI: 1.46-1.81) compared to those with the lowest genetic risk and lowest quartiles of PM2.5. Our findings highlight the importance of air pollution control in alleviating the burden of MD.

Spatial-temporal heterogeneity of air pollution and its relationship with meteorological factors in the Pearl River Delta, China

ABSTRACTPrevious analyses of continuously measured compounds in Fort McKay, an indigenous community in the Athabasca Oil Sands, have detected increasing concentrations of nitrogen dioxide (NO2) and total hydrocarbons (THC), but not of sulfur dioxide (SO2), ozone (O3), total reduced sulfur compounds (TRS), or particulate matter (aerodynamic diameter <2.5 μm; PM2.5). Yet the community frequently experiences odors, dust, and reduced air quality. The authors used Fort McKay’s continuously monitored air quality data (1998–2014) as a case study to assess techniques for air quality analysis that make no assumptions regarding type of change. Linear trend analysis detected increasing concentrations of higher percentiles of NO2, nitric oxide (NO), and nitrogen oxides (NOx), and THC. However, comparisons of all compounds between an early industrial expansion period (1998–2001) and current day (2011–2014) show that concentrations of NO2, SO2, THC, TRS, and PM2.5 have significantly increased, whereas concentrations of O3 are significantly lower. An assessment of the frequency and duration of periods when concentrations of each compound were above a variety of thresholds indicated that the frequency of air quality events is increasing for NO2 and THC. Assessment of change over time with odds ratios of the 25th, 50th, 75th, and 90th percentile concentrations for each compound compared with an estimate of natural background variability showed that concentrations of TRS, SO2, and THC are dynamic, higher than background, and changes are nonlinear and nonmonotonic. An assessment of concentrations as a function of wind direction showed a clear and generally increasing influence of industry on air quality. This work shows that evaluating air quality without assumptions of linearity reveals dynamic changes in air quality in Fort McKay, and that it is increasingly being affected by oil sands operations.Implications: Understanding the nature and types of air quality changes occurring in a community or region is essential for the development of appropriate air quality management policies. Time-series trending of air quality data is a common tool for assessing air quality changes and is often used to assess the effectiveness of current emission management programs. The use of this tool, in the context of oil sands development, has significant limitations, and alternate air quality change analysis approaches need to be applied to ensure that the impact of this development on air quality is fully understood so that appropriate emission management actions can be taken.

Candidate gene expression in response to low-level air pollution