SO2 and copper tolerance exhibit an evolutionary trade-off in Saccharomyces cerevisiae.

Copper tolerance and SO2 tolerance are two well-studied phenotypic traits of Saccharomyces cerevisiae. The genetic bases of these traits are the allelic expansion at the CUP1 locus and reciprocal translocation at the SSU1 locus, respectively. Previous work identified a negative association between SO2 and copper tolerance in S. cerevisiae wine yeasts. Here we probe the relationship between SO2 and copper tolerance and show that an increase in CUP1 copy number does not always impart copper tolerance in S. cerevisiae wine yeast. Bulk-segregant QTL analysis was used to identify variance at SSU1 as a causative factor in copper sensitivity, which was verified by reciprocal hemizygosity analysis in a strain carrying 20 copies of CUP1. Transcriptional and proteomic analysis demonstrated that SSU1 over-expression did not suppress CUP1 transcription or constrain protein production and provided evidence that SSU1 over-expression induced sulfur limitation during exposure to copper. Finally, an SSU1 over-expressing strain exhibited increased sensitivity to moderately elevated copper concentrations in sulfur-limited medium, demonstrating that SSU1 over-expression burdens the sulfate assimilation pathway. Over-expression of MET 3/14/16, genes upstream of H2S production in the sulfate assimilation pathway increased the production of SO2 and H2S but did not improve copper sensitivity in an SSU1 over-expressing background. We conclude that copper and SO2 tolerance are conditional traits in S. cerevisiae and provide evidence of the metabolic basis for their mutual exclusivity. These findings suggest an evolutionary driver for the extreme amplification of CUP1 observed in some yeasts.

Impact of ambient air pollution on lung function in preterm-born school-aged children.

RATIONALE: Increased outdoor air pollution worsens lung function in children. However, these associations are less well studied in preterm-born individuals. OBJECTIVES: We assessed associations between ambient air pollutants and spirometry measures in preterm-born children. METHODS: The Respiratory Health Outcomes in Neonates study recruited preterm-born children aged 7-12 years who were born at ≤34 week's gestation. We associated four ambient air pollutants (particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5), PM10, nitrogen dioxide (NO2) and sulfur dioxide) at time of birth and spirometry assessment and averaged exposure between these two time points with spirometry measures, using linear regression analyses. Gestational age was banded into 23-28, 29-31 and 32-34 week's. Regression models estimated spirometry values against pollutant levels at birth and at the time of spirometry. MEASUREMENTS AND MAIN RESULTS: From 565 preterm-born children, 542 (96%) had satisfactory data. After adjustments for early and current life factors, significant detrimental associations were noted between PM10 at birth and per cent predicted forced vital capacity (%FVC) for the 23-28 and 29-31 week's gestation groups and between current PM2.5 and NO2 exposure and %FVC for the 23-28 week's gestation group. No associations with spirometry were noted for the averaged pollution exposure between birth and spirometry. Predictive models showed 5.9% and 7.4% differences in %FVC between the highest and lowest current pollution exposures for PM2.5 and NO2, respectively, in the 23-28 week group. CONCLUSIONS: Birth and current exposures to road-traffic-associated pollutants detrimentally affected %FVC in preterm-born school-aged children, who already have compromised lung function.

Clearing the air: a review of the effects of air pollution on dialysis outcomes.

PURPOSE OF REVIEW: An evolving body of literature indicates exposure to air pollutants is associated with adverse health outcomes in dialysis patients. As the prevalence of kidney disease increases, understanding the role of environmental agents on the health of dialysis patients is critical to reducing global morbidity and mortality. RECENT FINDINGS: We identified 16 publications that investigated associations between pollutants including particulate matter (PM 2.5 and PM 10 ), carbon monoxide (CO), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), and ozone (O 3 ) and health outcomes among dialysis patients. Eight studies examined the effects of particulate matter (PM) and four studies examined the effects CO exposure on dialysis patients. Exposure to PM was consistently associated with outcomes including all-cause mortality and a smaller body of literature suggested relationships with subclinical outcomes. Exposure to CO was associated with all-cause mortality, generalized inflammation, and uremic pruritus. An additional four studies examined multiple pollutant exposures including NO 2 , SO 2 , and O 3 and reported associations with all-cause mortality in dialysis patients. SUMMARY: This review emphasized the nascent literature that demonstrates consistent relationships between air pollutant exposure and adverse outcomes among dialysis patients. Further research is needed to assess the impact of air pollutants, including how co-exposures will impact dialysis patient health.

Mitochondria-targeted fluorescent probe for simultaneously imaging viscosity and sulfite in inflammation models.

Many diseases in the human body are related to the overexpression of viscosity and sulfur dioxide. Therefore, it is essential to develop rapid and sensitive fluorescent probes to detect viscosity and sulfur dioxide. In the present work, we developed a dual-response fluorescent probe (ES) for efficient detection of viscosity and sulfur dioxide while targeting mitochondria well. The probe generates intramolecular charge transfer by pushing and pulling the electron-electron system, and the ICT effect is destroyed and the fluorescence quenched upon reaction with sulfite. The rotation of the molecule is inhibited in the high-viscosity system, producing a bright red light. In addition, the probe has good biocompatibility and can be used to detect sulfite in cells, zebrafish and mice, as well as upregulation of viscosity in LPS-induced inflammation models. We expect that the dual response fluorescent probe ES will be able to detect viscosity and sulfite efficiently, providing an effective means of detecting viscosity and sulfite-related diseases.

A rationally designed fluorescent probe for sulfur dioxide and its derivatives: applications in food analysis and bioimaging.

Exogenous sulfur dioxide (SO2) and its derivatives (SO32-/HSO3-) have been extensively utilized in food preservation and endogenous SO2 is recognized as a significant gaseous signaling molecule that can mediate various physiological processes. Overproduction and/or extensive intake of these species can trigger allergic reactions and even tissue damage. Therefore, it is highly desirable to monitor SO2 and its derivatives effectively and quantitatively both in vitro and in vivo. Herein, a new mitochondria-targeted fluorescent probe (PIB) had been constructed, which could ratiometrically recognize SO2 and its derivatives with excellent sensitivity (DL = 15.9 nM) and a fast response time (200 s). The obtained high selectivity and good adaptability of this SO2-specific probe in a wide pH range (6.5-10.0) allowed for quantitatively tracking of SO2 and its derivatives in real food samples (granulated sugar, crystal sugar, and white wine). In addition, PIB could locate at mitochondrion and was capable of imaging exogenous/endogenous SO2 in the cells and zebrafish. In particular, our findings represented one of the rare examples that have demonstrated endogenous SO2 is closely related with the apoptosis of cells. Importantly, probe PIB was successfully employed for in situ metabolic localization in mouse organs, implying the potential applications of our probe in further exploration on SO2-releated pathological and physiological processes.

Prenatal and early life exposure to air pollution and the risk of severe lower respiratory tract infections during early childhood: the Espoo Cohort Study.

BACKGROUND: There is inconsistent evidence of the effects of exposure to ambient air pollution on the occurrence of lower respiratory tract infections (LRTIs) in early childhood. We assessed the effects of individual-level prenatal and early life exposure to air pollutants on the risk of LRTIs in early life. METHODS: We studied 2568 members of the population-based Espoo Cohort Study born between 1984 and 1990 and living in 1991 in the City of Espoo, Finland. Exposure assessment was based on dispersion modelling and land-use regression for lifetime residential addresses. The outcome was a LRTI based on data from hospital registers. We applied Poisson regression to estimate the incidence rate ratio (IRR) of LTRIs, contrasting incidence rates in the exposure quartiles to the incidence rates in the first quartile. We used weighted quantile sum (WQS) regression to estimate the joint effect of the studied air pollutants. RESULTS: The risk of LRTIs during the first 2 years of life was significantly related to exposure to individual and multiple air pollutants, measured with the Multipollutant Index (MPI), including primarily sulphur dioxide (SO2), particulate matter with a dry diameter of up to 2.5 µm (PM2.5) and nitrogen dioxide (NO2) exposures in the first year of life, with an adjusted IRR of 1.72 per unit increase in MPI (95% CI 1.20 to 2.47). LRTIs were not related to prenatal exposure. CONCLUSIONS: We provide evidence that ambient air pollution exposure during the first year of life increases the risk of LRTIs during the first 2 years of life. SO2, PM2.5 and NO2 were found to contribute the highest weights on health effects.

A FRET-based multifunctional fluorescence probe for the simultaneous detection of sulfite and viscosity in living cells.

Viscosity and sulfur dioxide derivatives were significant indicators for the assessment of health threat and even cancers, therefore, on-site and real time detection of viscosity and sulfur dioxide derivatives has obtained considerable attentions. An FRET-based fluorescence probe JZX was designed and synthesized based on a novel energy donor of N,N-diethyl-4-(1H-phenanthro[9,10-d]imidazol-2-yl)benzamide fluorophore. JZX exhibited a large Stokes shift (230 nm), high energy transfer efficiency, wide emission channel gap (135 nm) and excellent stability and biocompatibility. JZX detected sulfur dioxide with low detection limit (55 nM), fast responding (16 min), high selectivity and sensitivity. Additionally, JZX tend to target endoplasmic reticulum of which normal metabolism will be disturbed by the abnormal levels of viscosity and sulfur dioxide derivatives. Prominently, JZX could concurrently detect viscosity and sulfur dioxide derivatives depending on different fluorescence signals in living cells for the screening of cancer cells. Hence, probe JZX will be a promising candidate for the detection of viscosity and sulfur dioxide derivatives, and even for the diagnosis of liver cancers.

Air pollution in Bialowieza forest: Analysis of short-term trends from 2014 to 2021.

Air pollution caused by sulphur dioxide (SO2) and nitrogen oxides (NOx) has negative impacts on forest health and can initiate forest dieback. Long-term monitoring and analysis of these pollution are carried out in Bialowieza Forest in NE Poland due to the threats from abiotic, biotic and anthropogenic factors. The main objective of our study was to monitor the levels and trends of air pollutant deposition in Bialowieza Forest. During a short-term monitoring period over six years (2014-2021), the concentration of SO2 in the air decreased significantly (from 2.03 µg m-3 in December 2015 to 0.20 µg m-3 in July 2016), while the concentration of NO2 in the air showed a non-significant decrease (from 8.24 µg m-3 in December 2015 to 1.61 µg m-3 May 2016). There was no significant linear trend in the wet deposition of S-SO4 anions. Mean monthly S-SO4 deposition varies between 4.54 and 94.14 mg m-2month-1. Wet nitrogen deposition, including oxidized nitrogen (N-NO3) and reduced nitrogen (N-NH4), showed a non-significant increase. Mean monthly precipitation of N-NO3 and N-N H4 ranged from 1.91 to 451.73 mg m-2month-1. Neither did total sulphur deposition nor total nitrogen deposition exceed the mean deposition values for forests in Europe (below 6 ha-1yr-1 and 3-15 ha-1yr-1, respectively). Our results indicate that air pollutants originate from local sources (households), especially from the village of Bialowieza, as demonstrated by the level and spatial distribution of air pollutant deposition. This indicates that air pollutants from the village of Bialowieza could spread to other parts of Bialowieza Forest in the future and may have a negative impact on forest health and can initiate forest dieback. It is therefore important to continue monitoring air pollution to assess the threats to this valuable forest ecosystem.

Artificial intelligence-based forecasting model for incinerator in sulfur recovery units to predict SO2 emissions.

Pollutant emissions from chemical plants are a major concern in the context of environmental safety. A reliable emission forecasting model can provide important information for optimizing the process and improving the environmental performance. In this work, forecasting models are developed for the prediction of SO2 emission from a Sulfur Recovery Unit (SRU). Since SRUs incorporate complex chemical reactions, first-principle models are not suitable to predict emission levels based on a given feed condition. Accordingly, artificial intelligence-based models such as standard machine learning (ML) algorithms, multi-layer perceptron (MLP), long short-term memory (LSTM), one-dimensional convolution (1D-CNN), and CNN-LSTM models were tested, and their performance was evaluated. The input features and hyperparameters of the models were optimized to achieve maximum performance. The performance was evaluated in terms of mean squared error (MSE) and mean absolute percentage Error (MAPE) for 1 h, 3 h and 5 h ahead of forecasting. The reported results show that the CNN-LSTM encoder-decoder model outperforms other tested models, with its superiority becoming more pronounced as the forecasting horizon increased from 1 h to 5 h. For the 5-h ahead forecasting, the proposed model showed a MAPE advantage of 17.23%, 4.41%, and 2.83%, respectively over the 1D-CNN, Deep LSTM, and single-layer LSTM models in the larger dataset.

Assessing the impact of environmental regulations on environmental performance in Chinese cities: A spatial Stochastic Frontier Model with endogeneity analysis.

The Chinese government has implemented environmental regulations to address the deterioration of air quality associated with rapid industrialization. However, there is no consensus on whether environmental regulations are beneficial to environmental performance. The technical challenges related to endogeneity and spatial correlation may bias the estimation of the emission reduction effect of regulations. In this study, we comprehensively evaluate the environmental performance of sulfur dioxide regulations in Chinese cities using a novel stochastic frontier model that introduces the single control function to correct estimation errors caused by spatial spillovers and endogeneity. Our analysis emphasizes that insufficient resolution of endogeneity or spatial spillovers may lead to underestimation or neglect of the environmental performance improvements achieved by these regulations. On the contrary, our revised research results indicate that regulations aimed at reducing sulfur dioxide emissions not only successfully control sulfur dioxide emissions, but also have a positive impact on reducing carbon emissions. In addition, we conduct in-depth research on the mechanisms by which environmental regulations improve performance by stimulating green technology innovation and promoting industrial structure upgrading. Based on our research findings, we propose policy recommendations to establish a city cooperation mechanism of technology exchange to achieve synergistic emission reduction and strengthen regional factor circulation.

Does "National Civilized City" policy mitigate air pollution in China? A spatial Durbin difference-in-differences analysis.

"National Civilized City" (NCC) is regarded as China's highest honorary title and most valuable city brand. To win and maintain the "golden city" title, municipal governments must pay close attention to various key appraisal indicators, mainly environmental ones. In this study we verify whether cities with the title are more likely to mitigate SO2 pollution. We adopt the spatial Durbin difference-in-differences (DID) model and use panel data of 283 Chinese cities from 2003 to 2018 to analyze the local (direct) and spillover effects (indirect) of the NCC policy on SO2 pollution. We find that SO2 pollution in Chinese cities is not randomly distributed in geography, suggesting the existence of spatial spillovers and possible biased estimates. Our study treats the NCC policy as a quasi-experiment and incorporates spatial spillovers of NCC policy into a classical DID model to verify this assumption. Our findings show: (1) The spatial distribution of SO2 pollution represents strong spatial spillovers, with the most highly polluted regions mainly situated in the North China Plain. (2) The Moran's I test results confirms significant spatial autocorrelation. (3) Results of the spatial Durbin DID models reveal that the civilized cities have indeed significantly mitigated SO2 pollution, indicating that cities with the honorary title are acutely aware of the environment in their bid to maintain the golden city brand. As importantly, we notice that the spatial DID term is also significant and negative, implying that neighboring civilized cities have also mitigated their own SO2 pollution. Due to demonstration and competition effects, neighboring cities that won the title ostensibly motivates local officials to adopt stringent policies and measures for lowering SO2 pollution and protecting the environment in competition for the golden title. The spatial autoregressive coefficient was significant and positive, indicating that SO2 pollution of local cities has been deeply affected by neighbors. A series of robustness check tests also confirms our conclusions. Policy recommendations based on the findings for protecting the environment and promoting sustainable development are proposed.

Effects of ambient air pollution on the risk of small- and large-for-gestational-age births: an analysis using national birth data in Japan.

OBJECTIVES: Small-for-gestational-age (SGA) and large-for-gestational-age (LGA) births are major adverse birth outcomes related to newborn health. In contrast, the association between ambient air pollution levels and SGA or LGA births has not been investigated in Japan; hence, the purpose of our study is to investigate this association. METHODS: We used birth data from Vital Statistics in Japan from 2017 to 2021 and municipality-level data on air pollutants, including nitrogen dioxide (NO2), sulfur dioxide (SO2), photochemical oxidants, and particulate matter 2.5 (PM2.5). Ambient air pollution levels throughout the first, second, and third trimesters, as well as the whole pregnancy, were calculated for each birth. The association between SGA/LGA and ambient levels of the air pollutants was investigated using crude and adjusted log-binomial regression models. In addition, a regression model with spline functions was also used to detect the non-linear association. RESULTS: We analyzed data from 2,434,217 births. Adjusted regression analyses revealed statistically significant and positive associations between SGA birth and SO2 level, regardless of the exposure period. Specifically, the risk ratio for average SO2 values throughout the whole pregnancy was 1.014 (95% confidence interval [CI] 1.009, 1.019) per 1 ppb increase. In addition, regression analysis with spline functions indicated that an increase in risk ratio for SGA birth depending on SO2 level was linear. Furthermore, statistically significant and negative associations were observed between LGA birth and SO2 except for the third trimester. CONCLUSIONS: It was suggested that ambient level of SO2 during the pregnancy term is a risk factor for SGA birth in Japan.

Mapping pollution exposure and chemistry during an extreme air quality event (the 2018 Kilauea eruption) using a low-cost sensor network.

Extreme air quality episodes represent a major threat to human health worldwide but are highly dynamic and exceedingly challenging to monitor. The 2018 Kilauea Lower East Rift Zone eruption (May to August 2018) blanketed much of Hawai'i Island in "vog" (volcanic smog), a mixture of primary volcanic sulfur dioxide (SO2) gas and secondary particulate matter (PM). This episode was captured by several monitoring platforms, including a low-cost sensor (LCS) network consisting of 30 nodes designed and deployed specifically to monitor PM and SO2 during the event. Downwind of the eruption, network stations measured peak hourly PM2.5 and SO2 concentrations that exceeded 75 µg m-3 and 1,200 parts per billion (ppb), respectively. The LCS network's high spatial density enabled highly granular estimates of human exposure to both pollutants during the eruption, which was not possible using preexisting air quality measurements. Because of overlaps in population distribution and plume dynamics, a much larger proportion of the island's population was exposed to elevated levels of fine PM than to SO2 Additionally, the spatially distributed network was able to resolve the volcanic plume's chemical evolution downwind of the eruption. Measurements find a mean SO2 conversion time of ∼36 h, demonstrating the ability of distributed LCS networks to observe reaction kinetics and quantify chemical transformations of air pollutants in a real-world setting. This work also highlights the utility of LCS networks for emergency response during extreme episodes to complement existing air quality monitoring approaches.

Spatial and temporal differentiation and its driving factors of air quality in the economic circle of Shandong Province during 2013-2020.

As the negative repercussions of environmental devastation, such as air quality decline and air pollution, become more apparent, environmental consciousness is growing across the world, forcing nations to take steps to mitigate the damage. China pledged to achieve air quality improvement goal to combat global environment issue, yet the spatial-temporal differentiation and its driving factors of environment-meteorology-economic index for air quality are not fully analysed. To promote regional collaborative control of air pollution and achieve sustainable urban development, spatial and temporal different and its driving factors of air quality in Shandong Province during 2013-2020. Results revealed that concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), and carbon monoxide (CO-95per) exhibited decreasing trend (SO2 concentrations decreasing 84 % and CO-95per concentrations decreasing 90 %). Air quality was improved from inland areas to coastal areas. Pollutant indicators of SO2, NO2, PM10, PM2.5, and CO-95per demonstrated significant positive correlation (P < 0.05). Air temperature and precipitation are significantly negatively correlated with concentrations of SO2, NO2, PM10, PM2.5, and CO-95per but significantly positively correlated with ozone (O3-8 h). SO2, NO2, PM2.5, PM10, CO-95per, and proportion of days with heavy pollution are strongly positively correlated with proportion of secondary industry but strongly negatively correlated with proportion of tertiary industry and volume of household waste. Except for O3-8 h, pollutant index of Provincial Capital Economic Circle (PCEC) and Southern Shandong Economic Circle (SSEC) has significant negative correlation (P < 0.05) with regional gross domestic product and investment in environmental protection; however, investment in environmental protection of Eastern Shandong Economic Circle (ESEC) has no significant correlation with air pollution index. There was significant negative correlation between vegetable sowing area and SSEC pollutant index. The relationship between pollution emission and investment in environmental protection has shifted from high pollution-low investment to low pollution-low investment in PCEC, ESEC and SSEC, and the inflection point was in 2020 for PCEC, 2019 for ESEC, and 2020 for SSEC. Those results provide empirical evidence and theoretical support for the improvement of regional air quality, aiming to achieve high-quality development. According to these findings, it has been found that meteorological elements, pollutant emission, socio-economic factors and agricultural data affect air quality. Those results could provide meaningful and significant supporting for synergistic regulation of diverse pollutants.

The relationship between air pollution and the occurrence of hypertensive disorders of pregnancy: Evidence from a study in Wuhan, China.

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.

Short-term exposure to sulfur dioxide and the occurrence of chronic obstructive pulmonary disease: An updated systematic review and meta-analysis based on risk of bias and certainty of evidence.

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.

Assessment of short-term effects of ambient air pollution exposure on osteoarthritis outpatient visits.

The association of short-term ambient air pollution exposure with osteoarthritis (OA) outpatient visits has been unclear and no study has assessed the modifying roles of district-level characteristics in the association between ambient air pollution exposure and OA outpatient visits. We investigated the cumulative associations of ambient air pollution exposure with daily OA outpatient visits and vulnerable factors influencing the associations using data from 16 districts of Beijing, China during 2013-2019. A total of 18,351,795 OA outpatient visits were included in the analyses. An increase of 10 µg/m3 in fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), maximum 8-hour moving-average ozone (8 h-O3), and 0.1 mg/m3 in carbon monoxide (CO) at representative lag days were associated with significant increases of 0.31 %, 0.06 %, 0.77 %, 0.87 %, 0.30 %, and 0.48 % in daily OA outpatient visits, respectively. Considerable OA outpatient visits were attributable to short-term ambient air pollution exposure. In addition, low temperature and high humidity aggravated ambient air pollution associated OA outpatient visits. District-level characteristics, such as population density, green coverage rate, and urbanization rate modified the risk of OA outpatient visits associated with air pollution exposure. These findings highlight the significance of controlling ambient air pollution during the urbanization process, which is useful in policy formation and implementation.

High altitude air pollution and respiratory disease: Evaluating compounded exposure events and interactions.

Today, air pollution remains a significant issue, particularly in high-altitude areas where its impact on respiratory disease remains incompletely explored. This study aims to investigate the association between various air pollutants and outpatient visits for respiratory disease in such regions, specifically focussing on Xining from 2016 to 2021. By analysing over 570,000 outpatient visits using a time-stratified case-crossover design and conditional logistic regression, we assessed the independent effects of pollutants like PM2.5, PM10, SO2, NO2, and CO, as well as their interactions. The evaluation of interactions employed measures such as relative excess odds due to interaction (REOI), attributable proportion due to interaction (AP), and synergy index (S). We also conducted a stratified analysis to identify potentially vulnerable populations. Our findings indicated that exposure to PM2.5, PM10, SO2, NO2, and CO significantly increased outpatient visits for respiratory disease, with odds ratios (ORs) of 2.40 % (95 % CI: 2.05 %, 2.74 %), 1.07 % (0.98 %, 1.16 %), 3.86 % (3.23 %, 4.49 %), 4.45 % (4.14 %, 4.77 %), and 6.37 % (5.70 %, 7.04 %), respectively. However, exposure to O3 did not show a significant association. We found significant interactions among PM2.5, SO2, NO2, and CO, where combined exposure further exacerbated the risk of respiratory diseases. For example, in the combination of PM2.5 and SO2, the REOI, AP, and S were 0.07 (95 % CI: 0.06, 0.09), 0.07 (0.06, 0.07), and 1.07 (1.05, 1.09), respectively. Additionally, elderly individuals and females were more sensitive to these pollutants, but no statistically significant interaction effects were observed between different age and gender groups. In conclusion, our study highlights the strong link between air pollution and respiratory disease in high-altitude areas, with combined pollutant exposure posing an even greater risk. It underscores the need for enhanced air quality monitoring and public awareness campaigns, particularly to protect vulnerable populations like the elderly and females.

Different effects of air pollutant concentrations on influenza A and B in Sichuan, China.

BACKGROUND: The detrimental effects of air pollution on the respiratory system are well documented. Previous research has established a correlation between air pollutant concentration and the frequency of outpatient visits for influenza-like illness. However, studies investigating the variations in infection among different influenza subtypes remain sparse. We aimed to determine the correlation between air pollutant levels and different influenza subtypes in Sichuan Province, China. METHODS: A generalized additive model and distributed lag nonlinear model were employed to assess the association between air pollutants and influenza subtypes, utilizing daily influenza data obtained from 30 hospitals across 21 cities in Sichuan Province. The analysis considered the temporal effects and meteorological factors. The study spanned from January 1, 2017, to December 31, 2019. To provide a more precise evaluation of the actual impact of air pollution on different subtypes of influenza, we also performed subgroup analyses based on factors such as gender, age, and geography within the population. RESULTS: During the investigation, 17,462 specimens from Sichuan Province tested positive for influenza. Among these, 12,607 and 4855 were diagnosed with Flu A and B, respectively. The related risk of influenza A infection significantly increased following exposure to PM2.5 on Lag2 days (RR=1.008, 95 % confidence interval [CI]: 1.000-1.016), SO2 and CO on Lag1 days (RR=1.121, 95 % CI: 1.032-1.219; RR=1.151, 95 % CI: 1.030-1.289), and NO2 on Lag0 day (RR=1.089, 95 % CI: 1.035-1.145). PM10 and SO2 levels on Lag0 day, PM2.5 levels on Lag1 day, and CO levels on Lag6 day, with a reduced risk of influenza B (RR=0.987, 95 % CI: 0.976-0.997; RR=0.817, 95 % CI: 0.676-0.987; RR=0.979, 95 % CI: 0.970-0.989; RR=0.814, 95 % CI: 0.561-0.921). CONCLUSION: The findings from the overall population and subgroup analyses indicated that the impact of air pollutant concentrations on influenza A and B is inconsistent, with influenza A demonstrating greater susceptibility to these pollutants. Minimizing the levels of SO2, CO, NO2, and PM2.5 can significantly decrease the likelihood of contracting influenza A. Analyzing the influence of environmental contaminants on different influenza subtypes can provide insights into seasonal influenza trends and guide the development of preventive and control strategies.

Estimation of Attributable Risk and Direct Medical and Non-Medical Costs of Major Mental Disorders Associated With Air Pollution Exposures Among Children and Adolescents in the Republic of Korea, 2011-2019.

BACKGROUND: Recent studies have reported the burden of attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and depressive disorder. Also, there is mounting evidence on the effects of environmental factors, such as ambient air pollution, on these disorders among children and adolescents. However, few studies have evaluated the burden of mental disorders attributable to air pollution exposure in children and adolescents. METHODS: We estimated the risk ratios of major mental disorders (ADHD, ASD, and depressive disorder) associated with air pollutants among children and adolescents using time-series data (2011-2019) obtained from a nationwide air pollution monitoring network and healthcare utilization claims data in the Republic of Korea. Based on the estimated risk ratios, we determined the population attributable fraction (PAF) and calculated the medical costs of major mental disorders attributable to air pollution. RESULTS: A total of 33,598 patients were diagnosed with major mental disorders during 9 years. The PAFs for all the major mental disorders were estimated at 6.9% (particulate matter < 10 µm [PM10]), 3.7% (PM2.5), and 2.2% (sulfur dioxide [SO2]). The PAF of PM10 was highest for depressive disorder (9.2%), followed by ASD (8.4%) and ADHD (5.2%). The direct medical costs of all major mental disorders attributable to PM10 and SO2 decreased during the study period. CONCLUSION: This study assessed the burden of major mental disorders attributable to air pollution exposure in children and adolescents. We found that PM10, PM2.5, and SO2 attributed 7%, 4%, and 2% respectively, to the risk of major mental disorders among children and adolescents.