Direct and cross impacts of upwind emission control on downwind PM2.5 under various NH3 conditions in Northeast Asia

Development of surface observation-based two-step emissions adjustment and its application on CO, NOx, and SO2 emissions in China and South Korea

It is challenging to estimate local emission conditions of a downwind area solely based on concentrations in the downwind area. This is because air pollutants that have a long residence time in the atmosphere can be transported over long distances and influence air quality in downwind areas. In this study, a Two-step Emissions Adjustment (TEA) approach was developed to adjust downwind emissions of target air pollutants with surface observations, considering their long-range transported emission impacts from upwind areas calculated from air quality simulations. Using the TEA approach, CO, NOx, and SO2 emissions were adjusted in China and South Korea between 2016 and 2021 based on existing bottom-up emissions inventories. Simulations with the adjusted emissions showed that the 6-year average normalized mean biases of the monthly mean concentrations of CO, NOx, and SO2 improved to 0.3 %, -2 %, and 2 %, respectively, in China, and to 5 %, 7 %, and 4 %, respectively, in South Korea. When analyzing the emission trends, it was estimated that the annual emissions of CO, NOx, and SO2 in China decreased at a rate of 7.2 %, 4.5 %, and 10.6 % per year, respectively. The decrease rate of emissions for each of these pollutants was similar to that of ambient concentrations. When considering upwind emission impacts in the emissions adjustment, CO emissions increased by 1.3 %/year in South Korea, despite CO concentrations in the country decreasing during the study period. During the study period, NOx and SO2 emissions in South Korea decreased by 3.9 % and 0.5 %/year, respectively. Moreover, the TEA approach can account for drastic short-term emission changes (e.g., social distancing due to COVID-19). Therefore, the TEA approach can be used to adjust emissions and improve reproducibility of concentrations of air pollutants suitable for health studies for areas where upwind emission impacts are significant.

Spatial-temporal heterogeneity and driving factors of PM2.5 in China: A natural and socioeconomic perspective

Background

Fine particulate matter (PM_2.5), one of the major atmospheric pollutants, has a significant impact on human health. However, the determinant power of natural and socioeconomic factors on the spatial-temporal variation of PM_2.5 pollution is controversial in China.

Methods

In this study, we explored spatial-temporal characteristics and driving factors of PM_2.5 through 252 prefecture-level cities in China from 2015 to 2019, based on the spatial autocorrelation and geographically and temporally weighted regression model (GTWR).

Results

PM_2.5 concentrations showed a significant downward trend, with a decline rate of 3.58 μg m^-3 a^-1, and a 26.49% decrease in 2019 compared to 2015, Eastern and Central China were the two regions with the highest PM_2.5 concentrations. The driving force of socioeconomic factors on PM_2.5 concentrations was slightly higher than that of natural factors. Population density had a positive significant driving effect on PM_2.5 concentrations, and precipitation was the negative main driving factor. The two main driving factors (population density and precipitation) showed that the driving capability in northern region was stronger than that in southern China. North China and Central China were the regions of largest decline, and the reason for the PM_2.5 decline might be the transition from a high environmental pollution-based industrial economy to a resource-clean high-tech economy since the implementation the Air Pollution Prevention and Control Action Plan in 2013.

Conclusion

We need to fully consider the coordinated development of population size and local environmental carrying capacity in terms of control of PM_2.5 concentrations in the future. This research is helpful for policy-makers to understand the distribution characteristics of PM_2.5 emission and put forward effective policy to alleviate haze pollution.

An observation-based adjustment method of regional contribution estimation from upwind emissions to downwind PM2.5 concentrations

We propose a method to adjust contributions from upwind emissions to downwind PM_2.5 concentrations to account for the differences between observed and simulated PM_2.5 concentrations in an upwind area. Emissions inventories (EI) typically have a time lag between the inventory year and the release year. In addition, traditional emission control policies and social issues such as the COVID-19 pandemic cause steady or unexpected changes in anthropogenic emissions. These uncertainties could result in overestimation of the emission impacts of upwind areas on downwind areas if emissions used in modeling for the upwind areas were larger than the reality. In this study, South Korea was defined as the downwind area while other regions in Northeast Asia including China were defined as the upwind areas to evaluate applicability of the proposed adjustment method. We estimated the contribution of emissions released from the upwind areas to PM_2.5 concentrations in South Korea from 2015 to 2020 using a three-dimensional photochemical model with two EIs. In these two simulations for 2015-2020, the annual mean foreign contributions differed by 4.1-5.5 µg/m³. However, after adjustment, the differences decreased to 0.4-1.1 µg/m³. The adjusted annual mean foreign contributions were 12.7 and 8.8 µg/m³ during 2015-2017 and 2018-2020, respectively. Finally, we applied the adjustment method to the COVID-19 pandemic period to evaluate the applicability for short-term episodes. The foreign contribution of PM_2.5 during the lockdown period in China decreased by 30% after adjustment and the PM_2.5 normalized mean bias in South Korea improved from 15% to -4%. This result suggests that the upwind contribution adjustment can be used to alleviate the uncertainty of the emissions inventory used in air quality simulations. We believe that the proposed upwind contribution adjustment method can help to correctly understand the contributions of local and upwind emissions to PM_2.5 concentrations in downwind areas.

Atmospheric ammonia and its effect on PM2.5 pollution in urban Chengdu, Sichuan Basin, China

Controlling ammonia (NH₃) emissions has been proposed as a strategy to mitigate haze pollution. To explore the role of NH₃ in haze pollution in Sichuan Basin, where agricultural activities are intense, hourly in situ data of NH₃, as well as nitric acid and secondary inorganic aerosols (SIAs) were gathered in Chengdu from April 2017 to March 2018. We found that NH₃ had an annual mean concentration of 9.7 ± 3.5 (mean ± standard deviation) μg m^-3, and exhibited seasonal variations (spring > summer > autumn and winter) due to changes in emission sources and meteorological conditions (particularly temperature). Chengdu's atmosphere is generally NH₃-sufficient, especially in the warm seasons, implying that the formation of SIAs is more sensitive to the availability of nitric acid. However, an NH₃ "sufficient-to-deficient" transition was found to occur during winter pollution periods, and the frequency of NH₃ deficiency increased with the aggravation of pollution. Under NH₃-deficient conditions, the nitrogen oxidation ratio increased linearly with the increase in free NH₃, implying that NH₃ contributes appreciably to the formation of nitrate and thus to high PM_2.5 loadings. No relationships of NH₃ with fossil fuel combustion-related pollutants were found. The NH₃ emissions from farmland and livestock waste in the suburbs of Chengdu and regional transport from west of Chengdu probably contribute to the occurrence of high PM_2.5 loading in winter and spring, respectively. These results suggest that to achieve effective mitigation of PM_2.5 in Chengdu, local and regional emission control of NH₃ and NOx synergistically would be effective.

Influencing factors of PM2.5 and O3 from 2016 to 2020 based on DLNM and WRF-CMAQ

In this study, distributed lag nonlinear models (DLNM) were built to characterize the non-linear exposure-lag-response relationship between the concentration of PM_2.5 and O₃ and multiple influencing factors, including basic meteorological elements and precursors. Then, a stratified analysis of different years, seasons, pollution levels, and wind direction was conducted. DLNMs and coupled Weather Research and Forecasting Model-Community Multi-scale Air Quality Model (WRF-CMAQ) were used to evaluate PM_2.5 and O₃ changes attributed to meteorological conditions and anthropogenic emissions comparing 2020 with 2016. As DLNMs showed, PM_2.5 pollution was promoted by low wind speed, high temperature, low humidity, and high concentrations of SO₂, NO₂, and O₃, among which NO₂ tended to be the dominant influencing factor. O₃ pollution was promoted by low wind speed, high temperature, low humidity, high concentration of PM_2.5 and low concentration of NO₂, among which temperature tended to be the dominant influencing factor. Moreover, north-south and easterly winds showed the greatest contribution to PM_2.5 and O₃, respectively. Both DLNMs and CMAQ showed that anthropogenic factors alleviated PM_2.5 pollution but aggravated O₃ pollution in 2020 in comparison with 2016, so did meteorological factors, but with smaller impacts. And anthropogenic influences were more evident in heavily polluted seasons for both PM_2.5 and O₃. This research may help understand the influencing factors of PM_2.5 and O₃ and provide scientific guide for abatement policies. Moreover, the good consistency in the results obtained from DLNMs and CMAQ indicated the reliability of the two models.

Exposure to High Aerial Ammonia Causes Hindgut Dysbiotic Microbiota and Alterations of Microbiota-Derived Metabolites in Growing Pigs

Ammonia, an atmospheric pollutant in the air, jeopardizes immune function, and perturbs metabolism, especially lipid metabolism, in human and animals. The roles of intestinal microbiota and its metabolites in maintaining or regulating immune function and metabolism are irreplaceable. Therefore, this study aimed to investigate how aerial ammonia exposure influences hindgut microbiota and its metabolites in a pig model. Twelve growing pigs were treated with or without aerial ammonia (35 mg/m³) for 25 days, and then microbial diversity and microbiota-derived metabolites were measured. The results demonstrated a decreasing trend in leptin (p = 0.0898) and reduced high-density lipoprotein cholesterol (HDL-C, p = 0.0006) in serum after ammonia exposure. Besides, an upward trend in hyocholic acid (HCA), lithocholic acid (LCA), hyodeoxycholic acid (HDCA) (p < 0.1); a downward trend in tauro-deoxycholic acid (TDCA, p < 0.1); and a reduced tauro-HDCA (THDCA, p < 0.05) level were found in the serum bile acid (BA) profiles after ammonia exposure. Ammonia exposure notably raised microbial alpha-diversity with higher Sobs, Shannon, or ACE index in the cecum or colon and the Chao index in the cecum (p < 0.05) and clearly exhibited a distinct microbial cluster in hindgut indicated by principal coordinate analysis (p < 0.01), indicating that ammonia exposure induced alterations of microbial community structure and composition in the hindgut. Further analysis displayed that ammonia exposure increased the number of potentially harmful bacteria, such as Negativibacillus, Alloprevotella, or Lachnospira, and decreased the number of beneficial bacteria, such as Akkermansia or Clostridium_sensu_stricto_1, in the hindgut (FDR < 0.05). Analysis of microbiota-derived metabolites in the hindgut showed that ammonia exposure increased acetate and decreased isobutyrate or isovalerate in the cecum or colon, respectively (p < 0.05). Unlike the alteration of serum BA profiles, cecal BA data showed that high ammonia exposure had a downward trend in cholic acid (CA), HCA, and LCA (p < 0.1); a downward trend in deoxycholic acid (DCA) and HDCA (p < 0.05); and an upward trend in glycol-chenodeoxycholic acid (GCDCA, p < 0.05). Mantel test and correlation analysis revealed associations between microbiota-derived metabolites and ammonia exposure-responsive cecal bacteria. Collectively, the findings illustrated that high ammonia exposure induced the dysbiotic microbiota in the hindgut, thereby affecting the production of microbiota-derived short-chain fatty acids and BAs, which play a pivotal role in the modulation of host systematic metabolism.

Sensitivity of fine particulate matter concentrations in South Korea to regional ammonia emissions in Northeast Asia

Ammonia (NH3) is an important precursor for forming PM2.5. In this study, we estimated the impact of upwind transboundary and local downwind NH3 emissions on PM2.5 and its inorganic components via photochemical grid model simulations. Nine sensitivity scenarios with ±50% perturbations of upwind (China) and/or downwind (South Korea) NH3 emissions were simulated for the year 2016 over Northeast Asia. The annual mean PM2.5 concentrations in the downwind area were predicted to change from -3.3 (-18%) to 2.4 μg/m3(13%) when the NH3 emissions in the upwind and downwind areas were perturbed by -50% to +50%. The change in PM2.5 concentrations in the downwind area depending on the change in NH3 emissions in the upwind area was the highest in spring, followed by winter. This was mainly attributed to the change in nitrate (NO3-), a secondary inorganic aerosol (SIA) that is a predominant constituent of PM2.5. Since NH3 is mainly emitted near the surface and vertical mixing is limited during the night, it was modeled that the aloft nitric acid (HNO3)-to-NO3- conversion in the morning hours was increased when the NH3 accumulated near the surface during nighttime begins to mix up within the Planetary Boundary Layer (PBL) as it develops after sunrise. This implies that the control of upwind and/or downwind NH3 emissions is effective at reducing PM2.5 concentrations in the downwind area even under NH3 rich conditions in Northeast Asia.