Extreme PM 2.5 pollution has become a significant environmental problem in China in recent years, which is hazardous to human health and daily life. Noticing the importance of investigating the causes of extreme PM 2.5 pollution, this paper classifies cities across China into eight categories (four groups plus two scenarios) based on the generalized extreme value (GEV) distribution using hourly station-level PM 2.5 concentration data, and a series of multi-choice models are employed to assess the probabilities that cities fall into different categories. Various factors such as precursor pollutants and socio-economic factors are considered after controlling for meteorological conditions in each model. It turns out that SO 2 concentration, NO 2 concentration, and population density are the top three factors contributing most to the log ratios. Moreover, in both left- and right-skewed cases, the influence of a one-unit increase of SO 2 concentration on the relative probability of cities falling into different groups shows an increasing trend, while those of NO 2 concentration show a decreasing trend. At the same time, the higher the extreme pollution level, the bigger the effect of SO 2 and NO 2 concentrations on the probability of cities falling into normalized scenarios. The multivariate logit model is used for prediction and policy simulations. In summary, by analyzing the influences of various factors and the heterogeneity of their influence patterns, this paper provides valuable insights in formulating effective emission reduction policies.

Ambient air pollution has been reported to be a risk factor for hypertensive disorders of pregnancy (HDP). Past studies have reported supportive evidence, but evidence from China is scarce and does not integrate the different periods of the pregnancy course. In this study, 1945 pregnant women with HDP and healthy pregnancies between 2016 and 2022 from the Renmin Hospital of Wuhan University registry network database were analysed. The geographic information, biological information and demographic information of the case were fused in the analysis. Machine learning methods were used to obtain the weight of the variable. Then, we used the generalized linear mixed model to evaluate the relationship between increased exposure to each pollutant at different periods of HDP and examined it in different groups. The results showed that SO2 had the predominate impact (12.65 %) on HDP compared with other air pollutants. SO2 exposure was associated with an increased risk of HDP. Increased unit SO2 concentrations were accompanied by an increased risk of HDP (OR = 1.33, 95 % CI: 1.13, 1.566), and the susceptible window for this effect was mainly in the first trimester (OR = 1.242, 95 % CI: 1.092, 1.412). In addition, SO2 exposure was associated with an increased risk of HDP in urban maternity (OR = 1.356, 95 % CI: 1.112, 1.653), obese maternity (OR = 3.58, 95 % CI: 1.608, 7.971), no higher education maternity (OR = 1.348, 95 % CI: 1.065, 1.706), nonzero delivery maternity (OR = 1.981, 95 % CI: 1.439, 2.725), maternal with first time maternity (OR = 1.247, 95 % CI: 1.007, 1.544) and other groups. In summary, SO2 exposure in early pregnancy is one of the risk factors for HDP, and the increased risk of HDP due to increased SO2 exposure may be more pronounced in obese, urban, low-education, and nonzero delivery populations.

BACKGROUND: Coal-fired power plants are major contributors of ambient sulfur dioxide (SO2) air pollution. Epidemiological literature suggests an adverse association between SO2 exposure during gestation and preterm birth (PTB; <37 weeks completed gestation). PTB is strongly associated with infant mortality and increased risk for later life morbidities.
OBJECTIVE: We investigated associations between SO2 and PTB in North Carolina and evaluated whether the associations were modified by race/ethnicity.
METHODS: We assembled a retrospective, administrative cohort of singleton births in North Carolina from 2003-2015. We used US EPA EQUATES data to assign long-term SO2 gestational exposures to eligible births for the entire pregnancy and by trimester. We used multivariable generalized linear regression to estimate risk differences (RD (95%CI)) per 1-ppb increase in SO2, adjusted for gestational parent education, Medicaid status, marital status, and season of conception. Multi-pollutant models were additionally adjusted for other criteria air co-pollutants (O3, PM2.5, NO2).
RESULTS: The median SO2 (24-hour average) across exposure windows was ~1.5 (IQR: 1.8) ppb. The overall baseline risk for PTB was 8,756 per 100,000 live births. When stratified by race/ethnicity, the baseline risk for PTB was 12215, 7824, and 7187 per 100,000 live births among non-Hispanic Black, non-Hispanic white, and Hispanic births, respectively. RDs per 1-ppb increase in SO2 averaged across the entire pregnancy were 317.0 (95%CI: 279.4, 354.5) and 568.2 (95%CI: 500.3, 636.1) per 100,000 live births for single- and multi-pollutant models, respectively. For the PTB multi-pollutant models, we observed similar RDs for non-Hispanic Black participants (669.6 [95%CI: 573.9, 765.2]) and non-Hispanic white participants (635.4 [95%CI: 557.2, 713.6]) with smaller RDs for Hispanic participants (336.8 [95%CI: 241.3, 432.2]).
CONCLUSIONS: The results for our adjusted single- and multi-pollutant models showed adverse associations between SO2 and PTB, with some evidence of effect measure modification by race/ethnicity within subcategories of PTB.

OBJECTIVE: To evaluate the influence of environmental factors and prematurity relating to juvenile dermatomyositis (JDM), its course and refractoriness to treatment.
METHODS: A case-control study with 35 patients followed up at a tertiary hospital and 124 healthy controls, all residents of São Paulo. Patients were classified according to monocyclic, polycyclic or chronic disease courses and refractoriness to treatment. The daily concentrations of pollutants (inhalable particulate matter-PM10, sulfur dioxide-SO2, nitrogen dioxide-NO2, ozone-O3 and carbon monoxide-CO) were provided by the Environmental Company of São Paulo. Data from the population were obtained through a questionnaire.
RESULTS: Fifteen patients had monocyclic courses, and 19 polycyclic/chronic courses. Eighteen patients were refractory to treatment. Maternal occupational exposure to inhalable agents (OR = 17.88; IC 95% 2.15-148.16, p = 0.01) and exposure to O3 in the fifth year of life (third tertile > 86.28µg/m3; OR = 6.53, IC95% 1.60-26.77, p = 0.01) were risk factors for JDM in the multivariate logistic regression model. The presence of a factory/quarry at a distance farther than 200 meters from daycare/school (OR = 0.22; IC 95% 0.06-0.77; p = 0.02) was a protective factor in the same analysis. Prematurity, exposure to air pollutants/cigarette smoke/sources of inhalable pollutants in the mother\'s places of residence and work during the gestational period were not associated with JDM. Prematurity, maternal exposure to occupational pollutants during pregnancy as well as patient\'s exposure to ground-level pollutants up to the fifth year of life were not associated with disease course and treatment refractoriness.
CONCLUSIONS: Risk factors for JDM were maternal occupational exposure and exposure to O3 in the fifth year of life.

Sulfur trioxide (SO3) as a condensable particle matter has a significant influence on atmospheric visibility, which easily arouses formation of haze. It is imperative to control the SO3 emission from the industrial flue gas. Three commonly used basic absorbents, including Ca(OH)2, MgO and NaHCO3 were selected to explore the effects of temperature, SO2 concentration on the SO3 absorption, and the reaction mechanism of SO3 absorption was further illustrated. The suitable reaction temperature for various absorbents were proposed, Ca(OH)2 at the high temperatures above 500°C, MgO at the low temperatures below 320°C, and NaHCO3 at the temperature range of 320-500°C. The competitive absorption between SO2 and SO3 was found that the addition of SO2 reduced the SO3 absorption on Ca(OH)2 and NaHCO3, while had no effect on MgO. The order of the absorption selectivity of SO3 follows MgO, NaHCO3 and Ca(OH)2 under the given conditions in this work. The absorption process of SO3 on NaHCO3 follows the shrinking core model, thus the absorption reaction continues until NaHCO3 was exhausted with the utilization rate of nearly 100%. The absorption process of SO3 on Ca(OH)2 and MgO follows the grain model, and the dense product layer hinders the further absorption reaction, resulting in low utilization of about 50% for Ca(OH)2 and MgO. The research provides a favorable support for the selection of alkaline absorbent for SO3 removal in application.

This investigation explored the potential of utilizing alkali-treated corn cob (CC) as a solid carbon source to improve NOX and SO2 removal from flue gas. Leaching experiments unveiled a hierarchy of chemical oxygen demand release capacity: 0.03 mol/L alkali-treated CC > 0.02 mol/L > 0.01 mol/L > 0.005 mol/L > control. In NOX and SO2 removal experiments, as the inlet NOX concentration rose from 300 to 1000 mg/m3, the average NOX removal efficiency increased from 58.56 % to 80.00 %. Conversely, SO2 removal efficiency decreased from 99.96 % to 91.05 %, but swiftly rebounded to 98.56 % by day 18. The accumulation of N intermediates (NH4+, NO3-, NO2-) increased with escalating inlet NOX concentration, while the accumulation of S intermediates (SO42-, SO32-, S0) varied based on shifts in the population of functional bacteria. The elevation in inlet NOX concentration stimulated the growth of denitrifying bacteria, enhancing NOX removal efficiency. Concurrently, the population of nitrate-reducing sulfur-oxidizing bacteria and sulfate-reducing bacteria expanded, aiding in the accumulation of S0 and the removal of SO2. The comparison experiments on carbon sources confirmed the comparable NOX and SO2 removal efficiencies of alkali-treated CC and glucose, yet underscored differences in intermediates accumulation due to distinct genus structures.

Impaired cognitive health is the leading cause of various disabilities and disorders. Air pollution has been dramatically increasing over the last few decades and has been identified as a potential risk factor for impaired cognitive health. This study investigates the effect of air pollutants, particulate matter (PM2.5, PM10), sulfur dioxide (SO2), and ground-level ozone, on global cognitive health. The data on environmental pollutants and cognitive health were recorded from PubMed, Web of Science, Scopus, and Google Scholar. Initially, 790 articles were identified after screening for duplicates and applying the inclusion and exclusion criteria, 21 studies were included, and data was synthesized to get a pooled result. The overall results revealed that increased exposure to PM2.5 was positively and significantly associated with cognitive decline (OR 1.49; 95% CI 1.11, 1.99; p = 0.01). The risk of cognitive impairment due to PM10 (OR 1.30; 95% CI 1.00-1.70, p = 0.05), and SO2 (OR 1.39; 95% CI 1.27-1.51; p < 0.01) exposure were also significantly heightened. The study findings show that overall exposure to particulate matter PM2.5, PM10, and SO2 was associated with an increased risk of a decrease in global cognitive functions. The findings suggest that reducing levels of air pollutants could be a strategic approach to mitigate cognitive health risks in populations worldwide.

Air pollution stands as an environmental risk to child mental health, with proven relationships hitherto observed only in urban areas. Understanding the impact of pollution in rural settings is equally crucial. The novelty of this article lies in the study of the relationship between air pollution and behavioural and developmental disorders, attention deficit hyperactivity disorder (ADHD), anxiety, and eating disorders in children below 15 living in a rural area. The methodology combines spatio-temporal models, Bayesian inference and Compositional Data (CoDa), that make it possible to study areas with few pollution monitoring stations. Exposure to nitrogen dioxide (NO2), ozone (O3), and sulphur dioxide (SO2) is related to behavioural and development disorders, anxiety is related to particulate matter (PM10), O3 and SO2, and overall pollution is associated to ADHD and eating disorders. To sum up, like their urban counterparts, rural children are also subject to mental health risks related to air pollution, and the combination of spatio-temporal models, Bayesian inference and CoDa make it possible to relate mental health problems to pollutant concentrations in rural settings with few monitoring stations. Certain limitations persist related to misclassification of exposure to air pollutants and to the covariables available in the data sources used.

Several studies have documented a relationship between short-term exposure to atmospheric sulfur dioxide (SO2) and chronic obstructive pulmonary disease (COPD). However, findings vary across different regions. This meta-analysis employed a random-effects model to calculate the combined risk estimate for each 10-μg/m3 increase in ambient SO2 concentration. Subgroup analysis aimed to identify sources of heterogeneity. To assess potential bias, studies were evaluated using a domain-based assessment tool developed by the World Health Organization. Sensitivity analyses, based on bias risk, explored how model assumptions influenced associations. An evidence certainty framework was used to evaluate overall evidence quality. The study protocol was registered with PROSPERO (CRD42023446823). We thoroughly reviewed 191 full-text articles, ultimately including 15 in the meta-analysis. The pooled relative risk for COPD was 1.26 (95 % CI 0.94-1.70) per 10-μg/m3 increase in ambient SO2. Eleven studies were deemed high risk due to inadequate handling of missing data. Overall evidence certainty was rated as medium. Given SO2\'s significant public health implications, continuous monitoring is crucial. Future research should include countries in Africa and Oceania to enhance global understanding of atmospheric SO2-related health issues.

The iron and steel industry (ISI) is a significant source of sulfur dioxide and particulate matter pollution in China. Existing research on regional environmental regulation or ISI emission reduction strategies tends to overlook spillover effects and the enterprise perspective. During the heating season, production limitations in ISI are potential policy measures for achieving structural emission reductions in heavily polluted cities in China\'s Jing-Jin-Ji and surrounding regions. We adopt a bottom-up modeling approach, incorporating effective production time to describe enterprise behavior and establishing a quantitative trade model based on trade theory. By modeling three types of production restriction policies outlined in policy documents, we evaluate the emission reduction effects of structure-adjustment measures using the example of reduced effective production time for steel-producing enterprises in the air pollution transmission channel in the Beijing-Tianjin-Hebei area. The results indicate the following: (1) Reducing the effective production time of ISI enterprises can help decrease domestic production value and total factor productivity in pollution-intensive industries, including but not limited to ISI. It also leads to reduced emissions of various pollutants in the implementation regions. (2) Due to interprovincial trade and input-output linkages, structural reduction measures in certain regions have implications for almost all other provinces\' industrial structures. Differences in initial industrial structures, factor endowments, and geographical locations contribute to varying directions and magnitudes of industrial structural changes. Pollution-intensive industries\' share tends to increase higher in less developed regions. (3) Our estimated pollution reduction is smaller compared to the literature evaluating clean air policies in similar regions using top-down strategies. This discrepancy arises because we analyze a single policy tool rather than modeling industry-wide emission fluctuations from the top down. Additionally, our modeling approach allows us to examine dynamic changes in comparative advantages. The increase in production scale for certain industries in policy-affected regions partially offsets the decline in pollution emissions. These findings enhance our understanding of structure-adjustment reduction measures\' role and highlight their potential advantages and limitations.