Smoke from wildfires poses a substantial threat to health in communities near and far. To mitigate the extent and potential damage of wildfires, prescribed burning techniques are commonly employed as land management tools; however, they introduce their own smoke-related risks. This study investigates the impact of prescribed fires on daily average PM2.5 and maximum daily 8-h averaged O3 (MDA8-O3) concentrations and estimates premature deaths associated with short-term exposure to prescribed fire PM2.5 and MDA8-O3 in Georgia and surrounding areas of the Southeastern US from 2015 to 2020. Our findings indicate that over the study domain, prescribed fire contributes to average daily PM2.5 by 0.94 ± 1.45 μg/m3 (mean ± standard deviation), accounting for 14.0% of year-round ambient PM2.5. Higher average daily contributions were predicted during the extensive burning season (January-April): 1.43 ± 1.97 μg/m3 (20.0% of ambient PM2.5). Additionally, prescribed burning is also responsible for an annual average increase of 0.36 ± 0.61 ppb in MDA8-O3 (approximately 0.8% of ambient MDA8-O3) and 1.3% (0.62 ± 0.88 ppb) during the extensive burning season. We estimate that short-term exposure to prescribed fire PM2.5 and MDA8-O3 could have caused 2665 (95% confidence interval (CI): 2249-3080) and 233 (95% CI: 148-317) excess deaths, respectively. These results suggest that smoke from prescribed burns increases the mortality. However, refraining from such burns may escalate the risk of wildfires; therefore, the trade-offs between the health impacts of wildfires and prescribed fires, including morbidity, need to be taken into consideration in future studies.

The 2022 wildfires in New Mexico, United States, were unparalleled compared to past wildfires in the state in both their scale and intensity, resulting in poor air quality and a catastrophic loss of habitat and livelihood. Among all wildfires in New Mexico in 2022, six wildfires were selected for our study based on the size of the burn area and their proximity to populated areas. These fires accounted for approximately 90 % of the total burn area in New Mexico in 2022. We used a regional chemical transport model and data-fusion technique to quantify the contribution of these six wildfires (April 6 to August 22) on particulate matter (PM2.5: diameter ≤ 2.5 μm) and ozone (O3) concentrations, as well as the associated health impacts from short-term exposure. We estimated that these six wildfires emitted 152 thousand tons of PM2.5 and 287 thousand tons of volatile organic compounds to the atmosphere. We estimated that the average daily wildfire smoke PM2.5 across New Mexico was 0.3 μg/m3, though 1 h maximum exceeded 120 μg/m3 near Santa Fe. Average wildfire smoke maximum daily average 8-h O3 (MDA8-O3) contribution was 0.2 ppb during the study period over New Mexico. However, over the state 1 h maximum smoke O3 exceeded 60 ppb in some locations near Santa Fe. Estimated all-cause excess mortality attributable to short term exposure to wildfire PM2.5 and MDA8-O3 from these six wildfires were 18 (95 % Confidence Interval (CI), 15-21) and 4 (95 % CI: 3-6) deaths. Additionally, we estimate that wildfire PM2.5 was responsible for 171 (95 %: 124-217) excess cases of asthma emergency department visits. Our findings underscore the impact of wildfires on air quality and human health risks, which are anticipated to intensify with global warming, even as local anthropogenic emissions decline.

This study was set in the Greater Toronto and Hamilton Area (GTHA), where commercial vehicle movements were assigned across the road network. Implications for greenhouse gas (GHG) emissions, air quality, and health were examined through an environmental justice lens. Electrification of light-, medium-, and heavy-duty trucks was assessed to identify scenarios associated with the highest benefits for the most disadvantaged communities. Using spatially and temporally resolved commercial vehicle movements and a chemical transport model, changes in air pollutant concentrations under electric truck scenarios were estimated at 1-km2 resolution. Heavy-duty truck electrification reduces ambient black carbon and nitrogen dioxide on average by 10 and 14%, respectively, and GHG emissions by 10.5%. It achieves the highest reduction in premature mortality attributable to fine particulate matter chronic exposure (around 200 cases per year) compared with light- and medium-duty electrification (less than 150 cases each). The burden of all traffic in the GTHA was estimated to be around 600 cases per year. The benefits of electrification accrue primarily in neighborhoods with a high social disadvantage, measured by the Ontario Marginalization Indices, narrowing the disparity of exposure to traffic-related air pollution. Benefits related to heavy-duty truck electrification reflect the adverse impacts of diesel-fueled freight and highlight the co-benefits achieved by electrifying this sector.

Hydroxymethanesulfonate (HMS) has been found to be an abundant organosulfur aerosol compound in the Beijing-Tianjin-Hebei (BTH) region with a measured maximum daily mean concentration of up to 10 μg per cubic meter in winter. However, the production medium of HMS in aerosols is controversial, and it is unknown whether chemical transport models are able to capture the variations of HMS during individual haze events. In this work, we modify the parametrization of HMS chemistry in the nested-grid GEOS-Chem chemical transport model, whose simulations provide a good account of the field measurements during winter haze episodes. We find the contribution of the aqueous aerosol pathway to total HMS is about 36% in winter in Beijing, due primarily to the enhancement effect of the ionic strength on the rate constants of the reaction between dissolved formaldehyde and sulfite. Our simulations suggest that the HMS-to-inorganic sulfate ratio will increase from the baseline of 7% to 13% in the near future, given the ambitious clean air and climate mitigation policies for the BTH region. The more rapid reductions in emissions of SO2 and NOx compared to NH3 alter the atmospheric acidity, which is a critical factor leading to the rising importance of HMS in particulate sulfur species.

Regional background ozone (O3_RBG) is an important component of surface ozone (O3). However, due to the uncertainties in commonly used Chemical Transport Models (CTMs) and statistical models, accurately assessing O3_RBG in China is challenging. In this study, we calculated the O3_RBG concentrations with the CTM - Brute Force Method (BFM) and constrained the results with site observations of O3 with the multiple linear regression (MLR) model. The annual average O3_RBG concentration in China region in 2020 is 35 ± 4 ppb, accounting for 81 ± 5 % of the maximum 8-h average O3 (MDA8 O3). We applied the random forest and Shapley additive explanations based on meteorological standardization techniques to separate the contributions of meteorology and natural emissions to O3_RBG. Natural emissions contribute more significantly to O3_RBG than meteorology in various Chineses regions (30-40 ppb), with higher contributions during the warm season. Meteorological factors show higher contributions in the spring and summer seasons (2-3 ppb) than the other seasons. Temperature and humidity are the primary contributors to O3_RBG in regions with severe O3 pollution in China, with their individual impacts ranging from 30 % to 62 % of the total impacts of all meteorological factors in different seasons. For policy implications, we tracked the contributions of O3_RBG and local photochemical reaction contributions (O3_LC) to total O3 concentration at different O3 levels. We found that O3_LC contribute over 45 % to MDA8 O3 on polluted days, supporting the current Chinese policy of reducing O3 peak concentrations by cutting down precursor emissions. However, as the contribution of O3_RBG is not considered in the policy, additional efforts are needed to achieve the control groal of O3 concentration. As the implementation of stringent O3 control measurements in China, the contribution of O3_RBG become increasingly significant, suggesting the need for attention to O3_RBG and regional joint prevention and control.

Surface ozone (O3) concentrations in China have increased largely in the past decade. An accurate understanding of O3 pollution evolution is critical for making effective regulatory policies. Here we integrate data- and process-based models to explore the drivers of the observed summertime surface O3 change in the North China Plain (NCP) over 2015-2021. The data-based model by the deep learning (DL) suggests the reverse of meteorological contributions to the observed O3 change, i.e., 0.14 ppb/y in 2015-2019 and -1.74 ppb/y in 2019-2021. This is mainly resulted from the reversed changes in meteorological variables in surface air temperature and relative humidity. The simulations from a global chemical transport model, GEOS-Chem, also support those results, i.e., the meteorological contribution to O3 changes are 0.26 ppb/y in 2015-2019 and -0.74 ppb/y in 2019-2021. Furthermore, our analysis exhibits possible weakened anthropogenic contributions to surface O3 rise, for example, 1.53 and 0.54 ppb/y by DL in 2015-2019 and 2019-2021, respectively. Similarly, GEOS-Chem simulations suggest an accelerated decrease in surface O3 concentrations driven by the decline in nitrogen dioxide (NO2) concentrations, i.e., approximately 0.4 and 1.2 ppb in 2015-2019 and 2019-2021, respectively. The combined effects of meteorological and anthropogenic contributions led to a significant decrease in surface O3 concentrations by -1.20 ppb/y in 2019-2021. The findings in this work offer valuable insights to mitigate O3 pollution in China.

Objectives: The study estimated the environmental burden of disease (EBD) attributable to a long-term exposure of the population to nitrogen dioxide (NO2) and fine particulate matter (PM2.5) emissions from hard coal- and lignite-fired power plants in Germany for the year 2015. Methods: The contribution of coal-fired power plants to the total air pollutant concentration was modelled using a chemical transport model and then combined with population data to assess the corresponding population exposure. We calculated years of life lost (YLL), years of life with disability, or disability-adjusted life years for different health outcomes with a strong evidence for an association with the exposure. Results: The burden of disease from PM2.5 emissions from lignite is 1.2 times higher than that from hard coal emissions (7,866 YLL compared to 6,412 YLL). NO2 emissions from lignite, cause a burden of disease 2.3 times higher than hard coal NO2-emission (13,537 YLL compared to 5,906 YLL). The EBD for both pollutants is dominated by diseases of the cardiovascular system. Conclusion: Abandoning energy generation by coal-fired power plants would lower the burden of disease in Germany.

Nitrous acid (HONO) plays an important role in the budget of hydroxyl radical (OH) in the atmosphere. However, current chemical transport models (CTMs) typically underestimate ambient concentrations of HONO due to a dearth of high resolution primary HONO emission inventories. To address this issue, we have established a highly resolved bottom-up HONO emission inventory for CTMs in Guangdong province, utilizing the best available domestic measured emission factors and newly obtained activity data. Our results indicate that emissions from various sources in 2020, including soil, on-road traffic, non-road traffic, biomass burning, and stationary combustion, were estimated at 21.5, 10.0, 8.2, 2.5, and 0.7 kt, respectively. Notably, the HONO emissions structure differed between the Pearl River Delta (PRD) and the non-PRD regions. Specifically, traffic sources were the dominant contributors (62 %) to HONO emissions in the PRD, whereas soil sources accounted for the majority (65 %) of those in the non-PRD. Among on-road traffic sources, diesel vehicles played a significant role, contributing 99.7 %. Comparisons with previous methods suggest that HONO emissions from diesel vehicles are underestimated by approximately 2.5 times. Higher HONO emissions, dominated by soil emissions, were observed in summer months, particularly in August. Furthermore, diesel vehicle emissions were pronounced at night, likely contributing to the nighttime accumulation of HONO and the morning peak of OH. The emission inventories developed in this study can be directly applied to widely used CTMs, such as CMAQ, CAMx, WRF-Chem, and NAQPMS, to support the simulation of OH formation and secondary air pollution.

In this study, we estimated the future emission inventory of primary air pollutants in Japan in 2050 after introducing low-carbon technology based on the results of the socio-economic model provided by the Japanese government. The results suggested that introducing net-zero carbon technology would contribute to a 50-60 % decrease in primary NOx, SO2, and CO emissions and a ~30 % decrease in primary emissions of volatile organic compounds (VOCs) and PM2.5. The estimated emission inventory and future meteorological conditions in 2050 were applied as inputs to a chemical transport model. A scenario involving the application of future reduction strategies with relatively moderate global warming (RCP4.5) was evaluated. The results showed that the concentration of tropospheric ozone (O3) was highly reduced compared with that in 2015 after applying net-zero carbon reduction strategies. On the other hand, the fine particulate matter (PM2.5) concentration under the 2050 scenario was expected to be equal or higher because of the growth in secondary aerosol formation caused by the increase in short-wave radiation. Finally, the premature mortality change from 2015 to 2050 was analyzed, and the change in air quality contributed by net-zero carbon technology will contribute to a ~4000 decrease in premature deaths in Japan.

The ability of a chemical transport model to simulate accurate meteorological and chemical processes depends upon the physical parametrizations and quality of meteorological input data such as initial/boundary conditions. In this study, weather research and forecasting model coupled with chemistry (WRF-Chem) is used to test the sensitivity of PM2.5 predictions to planetary boundary layer (PBL) parameterization schemes (YSU, MYJ, MYNN, ACM2, and Boulac) and meteorological initial/boundary conditions (FNL, ERA-Interim, GDAS, and NCMRWF) over Indo-Gangetic Plain (Delhi, Punjab, Haryana, Uttar Pradesh, and Rajasthan) during the winter period (December 2017 to January 2018). The aim is to select the model configuration for simulating PM2.5 which shows the lowest errors and best agreement with the observed data. The best results were achieved with initial/boundary conditions from ERA and GDAS datasets and local PBL parameterization (MYJ and MYNN). It was also found that PM2.5 concentrations are relatively less sensitive to changes in initial/boundary conditions but in contrast show a stronger sensitivity to changes in the PBL scheme. Moreover, the sensitivity of the simulated PM2.5 to the choice of PBL scheme is more during the polluted hours of the day (evening to early morning), while that to the choice of the meteorological input data is more uniform and subdued over the day. This work indicates the optimal model setup in terms of choice of initial/boundary conditions datasets and PBL parameterization schemes for future air quality simulations. It also highlights the importance of the choice of PBL scheme over the choice of meteorological data set to the simulated PM2.5 by a chemical transport model.