Exploring the association between atmospheric pollutants and preterm birth risk in a river valley city.

Objective: To investigate the association between exposure to atmospheric pollutants and preterm birth in a river valley-type city and its critical exposure windows. Methods: A retrospective cohort study was used to collect data from the medical records of preterm and full-term deliveries in two hospitals in urban areas of a typical river valley-type city from January 2018 to December 2019. A total of 7,288 cases were included in the study with general information such as pregnancy times, the number of cesarean sections, occupation, season of conception and regularity of the menstrual cycle. And confounding factors affecting preterm birth were inferred using the chi-square test. The effects of exposure to each pollutant, including particulate matter 2.5 (PM2.5), particulate matter 10 (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO) and ozone (O3), during pregnancy on preterm birth and the main exposure windows were explored by establishing a logistic regression model with pollutants introduced as continuous variables. Results: Maternal age, pregnancy times, number of births, number of cesarean sections, season of conception, complications diseases, comorbidities diseases, hypertension disorder of pregnancy and neonatal low birth weight of the newborn were significantly different between preterm and term pregnant women. Logistic regression analysis after adjusting for the above confounders showed that the risk of preterm birth increases by 0.9, 0.6, 2.4% in T2 and by 1.0, 0.9, 2.5% in T3 for each 10 µg/m3 increase in PM2.5, PM10, NO2 concentrations, respectively. The risk of preterm birth increases by 4.3% in T2 for each 10 µg/m3 increase in SO2 concentrations. The risk of preterm birth increases by 123.5% in T2 and increases by 188.5% in T3 for each 10 mg/m3 increase in CO concentrations. Conclusion: Maternal exposure to PM2.5, PM10, NO2, CO was associated with increased risk on preterm birth in mid-pregnancy (T2) and late pregnancy (T3), SO2 exposure was associated with increased risk on preterm birth in mid-pregnancy (T2).

Investigating the impact of urban-environmental factors on air pollutants: a land use regression model approach and health risk assessment.

The presence of pollutants in the earth's atmosphere has a direct impact on human health and the environment. So that pollutants such as carbon monoxide (CO) and particulate matter (PM) cause respiratory diseases, cough headache, etc. Since the amount of pollutants in the air is related to environmental and urban factors, the aim of the current research is to investigate the relationship between the concentration of CO, PM2.5 and PM10 with urban-environmental factors including land use, wind speed and wind direction, topography, traffic, road network, and population through a Land use regression (LUR) model. The concentrations of CO, PM2.5 and PM10 were measured during four seasons from 26th of March 2022 to 16th of March 2023 at 25 monitoring stations and then the information about pollutant measurement points and Land use data were entered into the ArcGIS software. The annual average concentrations of CO, PM2.5 and PM10 were 0.7 ppm, 18.94 and 60.76 µg/m3, respectively, in which the values of annual average concentration of CO and PMs were outside the air quality guideline standard. The results of the health risk assessment showed that the hazard quotient values for all three investigated pollutants were lower than 1 and therefore, they were not in adverse conditions in terms of health effects. Among the urban-environmental factors affecting air pollution, the traffic variable is the most important factor affecting the annual LUR model of CO, PM2.5 and PM10, and then the topography variable is the second most effective factor on the annual LUR model of the aforementioned pollutants.

Effectiveness of Text Messaging in Encouraging Smoking Cessation among Non-Communicable Disease Patients: A Randomized Controlled Trial.

OBJECTIVE: To assess the impact of a text-messaging intervention on smoking cessation among patients with non-communicable diseases. METHODS: A total of 200 participants were randomly assigned to either a text-messaging intervention group or a control group. The 7-day point prevalence of smoking cessation and exhaled carbon monoxide (CO) levels were measured at baseline, 6 weeks, and 18 weeks. Mixed linear regression was employed to examine the interaction effect of exhaled CO between the intervention group and follow-up time. RESULTS: The 7-day point prevalence of smoking cessation increased by 16.16% (95% CI: 10.98, 21.33) at the 6-week follow-up and by 15.46% (95% CI: 10.68, 21.33) at the 18-week follow-up. In the intervention group, exhaled CO was significantly lower compared to the control group at 6 weeks (mean difference: -5.79; 95% CI: -7.26, -4.32) and at 18 weeks (mean difference: -4.19; 95% CI: -5.67, -2.71). CONCLUSION: The text-messaging intervention proved effective in increasing the prevalence of smoking cessation and reducing carbon monoxide levels among non-communicable disease patients.

Similar experimental study on diffusion and distribution of diesel exhaust in confined space of a coal mine.

In the confined space of the underground coal mine, which is dominated by transportation lanes, explosion-proof diesel-powered trackless rubber-wheeled vehicles are becoming the main transportation equipment, and the exhaust gas produced by them is hazardous to the health of workers and pollutes the underground environment. In this experiment, a similar test platform is built to study the effects of wind speed, vehicle speed, and different wind directions on the diffusion characteristics of exhaust gas. In this paper, CO and SO2 are mainly studied. The results show that the diffusion of CO and SO2 gas is similar and the maximum SO2 concentration only accounts for 11.4% of the CO concentration. Exhaust gas is better diluted by increasing the wind speed and vehicle speed, respectively. Downwind is affected by the reverse wind flow and diffuses to the driver's position, which is easy to cause occupational diseases. When the wind is a headwind, the exhaust gases spread upwards and make a circumvention movement, gathering at the top. When the wind speed and vehicle speed are both 0.6 m/s, the CO concentration corresponds to the change trend of the Lorentz function when the wind is downwind and the CO concentration corresponds to the change trend of the BiDoseResp function when the wind is headwind. The study of exhaust gas diffusion characteristics is of great significance for the subsequent purification of the air in the restricted mine space and the protection of the workers' occupational health.

Binder-type effect on the physico-mechanical, combustion and emission properties of Alstonia boonei De Wild. sawdust and Theobroma cacao L. pod biochar briquettes for energy applications.

Energy application potential from the abundant biomass residues is inadequately exploited. Over-dependence on forest trees, its negative environmental impacts, and ever-rising energy costs require alternative production technologies including briquetting. The physico-mechanical and combustion properties of binderless and bindered Alstonia boonei sawdust and Theobroma cacao (cocoa) pod briquettes, carbonized in a steel kiln (at 410±5°C, and a heating rate of 4°C/min from the ambient temperature of 25°C), piston-pressed at 9.0 MPa, were studied. The binders were starch, wax, and clay. Starch-bindered T. cacao pod briquettes recorded the maximum bulk density (640 kg/cm3), while basic density was greatest for sawdust/clay briquette (433 kg/cm3). Sawdust/wax briquette produced much Water Resistance Capacity (76.76%) with safer carbon monoxide (CO) emissions (0.67 ppm). A. boonei sawdust/starch briquettes recorded the greatest calorific value (24.023 MJ/kg), least specific fuel consumption (0.0483 kg/l), and slowest burning rate (0.0005 kg/min). All but T. cacao pod/starch and Sawdust/starch emitted CO below the safe air quality Standard of ≤ 6ppm (24h mean). Binderless sawdust, sawdust/starch and T. cacao pod/starch briquettes recorded 47.86, 20.95 and 11.40 µg/m3 particulate matter (PM2.5) respectively, which are below WHO Air Quality Standard safe for domestic uses. Binderless T. cacao pod produced more harmful CO and PM2.5 than its non-bindered A. boonei sawdust counterpart. Clay-bindered briquettes were the most durable. Briquetting, 'a waste-to-energy technology', enhances bio-residue management for domestic and industrial spaces in the global energy mix.

Random Forest and Feature Importance Measures for Discriminating the Most Influential Environmental Factors in Predicting Cardiovascular and Respiratory Diseases.

BACKGROUND: Several studies suggest that environmental and climatic factors are linked to the risk of mortality due to cardiovascular and respiratory diseases; however, it is still unclear which are the most influential ones. This study sheds light on the potentiality of a data-driven statistical approach by providing a case study analysis. METHODS: Daily admissions to the emergency room for cardiovascular and respiratory diseases are jointly analyzed with daily environmental and climatic parameter values (temperature, atmospheric pressure, relative humidity, carbon monoxide, ozone, particulate matter, and nitrogen dioxide). The Random Forest (RF) model and feature importance measure (FMI) techniques (permutation feature importance (PFI), Shapley Additive exPlanations (SHAP) feature importance, and the derivative-based importance measure (κALE)) are applied for discriminating the role of each environmental and climatic parameter. Data are pre-processed to remove trend and seasonal behavior using the Seasonal Trend Decomposition (STL) method and preliminary analyzed to avoid redundancy of information. RESULTS: The RF performance is encouraging, being able to predict cardiovascular and respiratory disease admissions with a mean absolute relative error of 0.04 and 0.05 cases per day, respectively. Feature importance measures discriminate parameter behaviors providing importance rankings. Indeed, only three parameters (temperature, atmospheric pressure, and carbon monoxide) were responsible for most of the total prediction accuracy. CONCLUSIONS: Data-driven and statistical tools, like the feature importance measure, are promising for discriminating the role of environmental and climatic factors in predicting the risk related to cardiovascular and respiratory diseases. Our results reveal the potential of employing these tools in public health policy applications for the development of early warning systems that address health risks associated with climate change, and improving disease prevention strategies.

Assessing long-term effects of gaseous air pollution exposure on mortality in the United States using a variant of difference-in-differences analysis.

Long-term mortality effects of particulate air pollution have been investigated in a causal analytic frame, while causal evidence for associations with gaseous air pollutants remains extensively lacking, especially for carbon monoxide (CO) and sulfur dioxide (SO2). In this study, we estimated the causal relationship of long-term exposure to nitrogen dioxide (NO2), CO, SO2, and ozone (O3) with mortality. Utilizing the data from National Morbidity, Mortality, and Air Pollution Study, we applied a variant of difference-in-differences (DID) method with conditional Poisson regression and generalized weighted quantile sum regression (gWQS) to investigate the independent and joint effects. Independent exposures to NO2, CO, and SO2 were causally associated with increased risks of total, nonaccidental, and cardiovascular mortality, while no evident associations with O3 were identified in the entire population. In gWQS analyses, an interquartile range-equivalent increase in mixture exposure was associated with a relative risk of 1.067 (95% confidence interval: 1.010-1.126) for total mortality, 1.067 (1.009-1.128) for nonaccidental mortality, and 1.125 (1.060-1.193) for cardiovascular mortality, where NO2 was identified as the most significant contributor to the overall effect. This nationwide DID analysis provided causal evidence for independent and combined effects of NO2, CO, SO2, and O3 on increased mortality risks among the US general population.

Smoking reduction using an electronic nicotine delivery system (ENDS) with nicotine delivery similar to combustible cigarettes.

BACKGROUND: Electronic nicotine delivery systems (ENDS) offer a promising approach to tobacco harm reduction, but many people use both ENDS and combustible cigarettes ("dual use"), which undermines potential risk reduction. To explore the role of ENDS nicotine delivery in promoting switching to ENDS, we conducted a study in which people who smoked cigarettes were offered an ENDS that had previously been shown to replicate the rapid nicotine pharmacokinetics of combustible cigarettes (BIDI® Stick). METHODS: Twenty-five cigarette smoking adults, not seeking smoking cessation treatment, but open to using ENDS as a cigarette substitute, were provided with a 12-week supply of BIDI® Stick in tobacco or menthol flavors, during a study that included seven biweekly sessions and a 6-month follow-up. Daily diaries assessed ENDS and cigarette use, and exhaled carbon monoxide (eCO) served as an objective marker of smoke intake. Subjective ratings were collected to assess the rewarding properties of ENDS and combustible cigarettes, and indices of nicotine dependence. RESULTS: Over 12 weeks, ENDS use increased to an average of 15.8 occasions per day (SD = 20.2) and self-reported cigarette consumption decreased by 82% from 16.7 cigarettes/day (SD = 6.0) at baseline to 3.0 cigarettes/day (SD = 4.1) at week 12. The eCO level decreased by 27% from an average of 20.0 ppm (SD = 9.8) at baseline to 14.5 ppm (SD = 9.9) at week 12. Four of 25 participants completely switched to ENDS and were smoking abstinent during weeks 9-12. At 6 months one participant was confirmed to be abstinent. Ratings of subjective reward for the ENDS were very similar to those of participants' usual brands of cigarettes. Dependence level was lower for the ENDS than for combustible cigarettes. CONCLUSIONS: In this study, the ENDS effectively replicated the subjective rewarding effects of participants' usual brands of cigarettes and led to a substantial reduction in reported cigarettes/day. Exhaled CO showed less of a decrease, possibly due to compensatory smoking behavior and/or the timing of eCO measurements that might not have reflected smoke intake throughout the day. The relatively low rate of sustained smoking abstinence at 6 months suggests that additional approaches continue to be needed for achieving higher rates of complete switching. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT05855343.

Fe(III)-Based Fluorescent Probe for High-Performance Recognition, Test Strip Analysis, and Cell Imaging of Carbon Monoxide.

Fluorescence sensing and imaging techniques are being widely studied for detecting carbon monoxide (CO) in living organisms due to their speed, sensitivity, and ease of use to biological systems. Most fluorescent probes used for this purpose are based on heavy metal ions like Pd, with a few using elements like Ru, Rh, Ir, Os, Tb, and Eu. However, these metals can be expensive and toxic to cells. There is a need for more affordable and biologically safe fluorescent probes for CO detection. Drawing inspiration from the robust affinity exhibited by heme iron toward CO, in this work, a rhodamine derivative called RBF was developed for imaging CO in living cells by binding to Fe(III) and could be used for CO sensing. A Fe(III)-based fluorescent probe for CO imaging in living cells offers advantages of cost effectiveness, low toxicity, and ease of use. The fluorescence detection using the RBF-Fe system showed a direct correlation with increasing levels of CORM-3 (LOD = 146 nM) or the exposure time of CO gas, displaying reduced fluorescence. A CO test paper based on RBF-Fe was created for simple on-site CO detection, where fluorescence would diminish in response to CO exposure, allowing rapid (2 min) visual identification. Imaging of CO in living cells was successfully conducted using the probe system, showing a decrease in fluorescence intensity as CORM-3 concentrations increased, indicating its effectiveness in monitoring CO levels accurately within living cells.

SMMP: A Deep-Coverage Marine Metaproteome Method for Microbial Community Analysis throughout the Water Column Using 1 L of Seawater.

Marine microbes drive pivotal transformations in planetary-scale elemental cycles and have crucial impacts on global biogeochemical processes. Metaproteomics is a powerful tool for assessing the metabolic diversity and function of marine microbes. However, hundreds of liters of seawater are required for normal metaproteomic analysis due to the sparsity of microbial populations in seawater, which poses a substantial challenge to the widespread application of marine metaproteomics, particularly for deep seawater. Herein, a sensitive marine metaproteomics workflow, named sensitive marine metaproteome analysis (SMMP), was developed by integrating polycarbonate filter-assisted microbial enrichment, solid-phase alkylation-based anti-interference sample preparation, and narrow-bore nanoLC column for trace peptide separation and characterization. The method provided more than 8500 proteins from 1 L of bathypelagic seawater samples, which covered diverse microorganisms and crucial functions, e.g., the detection of key enzymes associated with the Wood-Ljungdahl pathway. Then, we applied SMMP to investigate vertical variations in the metabolic expression patterns of marine microorganisms from the euphotic zone to the bathypelagic zone. Methane oxidation and carbon monoxide (CO) oxidation were active processes, especially in the bathypelagic zone, which provided a remarkable energy supply for the growth and proliferation of heterotrophic microorganisms. In addition, marker protein profiles detected related to ammonia transport, ammonia oxidation, and carbon fixation highlighted that Thaumarchaeota played a critical role in primary production based on the coupled carbon-nitrogen process, contributing to the storage of carbon and nitrogen in the bathypelagic regions. SMMP has low microbial input requirements and yields in-depth metaproteome analysis, making it a prospective approach for comprehensive marine metaproteomic investigations.

Particulate and gaseous air pollutants exceed WHO guideline values and have the potential to damage human health in Faisalabad, Metropolitan, Pakistan.

First-ever measurements of particulate matter (PM2.5, PM10, and TSP) along with gaseous pollutants (CO, NO2, and SO2) were performed from June 2019 to April 2020 in Faisalabad, Metropolitan, Pakistan, to assess their seasonal variations; Summer 2019, Autumn 2019, Winter 2019-2020, and Spring 2020. Pollutant measurements were carried out at 30 locations with a 3-km grid distance from the Sitara Chemical Industry in District Faisalabad to Bhianwala, Sargodha Road, Tehsil Lalian, District Chiniot. ArcGIS 10.8 was used to interpolate pollutant concentrations using the inverse distance weightage method. PM2.5, PM10, and TSP concentrations were highest in summer, and lowest in autumn or winter. CO, NO2, and SO2 concentrations were highest in summer or spring and lowest in winter. Seasonal average NO2 and SO2 concentrations exceeded WHO annual air quality guide values. For all 4 seasons, some sites had better air quality than others. Even in these cleaner sites air quality index (AQI) was unhealthy for sensitive groups and the less good sites showed Very critical AQI (> 500). Dust-bound carbon and sulfur contents were higher in spring (64 mg g-1) and summer (1.17 mg g-1) and lower in autumn (55 mg g-1) and winter (1.08 mg g-1). Venous blood analysis of 20 individuals showed cadmium and lead concentrations higher than WHO permissible limits. Those individuals exposed to direct roadside pollution for longer periods because of their occupation tended to show higher Pb and Cd blood concentrations. It is concluded that air quality along the roadside is extremely poor and potentially damaging to the health of exposed workers.

IoT-based monitoring system and air quality prediction using machine learning for a healthy environment in Cameroon.

This paper is aimed at developing an air quality monitoring system using machine learning (ML), Internet of Things (IoT), and other elements to predict the level of particulate matter and gases in the air based on the air quality index (AQI). It is an air quality assessor and therefore a means of achieving the Sustainable Development Goals (SDGs), in particular, SDG 3.9 (substantial reduction of the health impacts of hazardous substances) and SDG 11.6 (reduction of negative impacts on cities and populations). AQI quantifies and informs the public about air pollutants and their adverse effects on public health. The proposed air quality monitoring device is low-cost and operates in real-time. It consists of a hardware unit that detects various pollutants to assess air quality as well as other airborne particles such as carbon dioxide (CO2), methane (CH4), volatile organic compounds (VOCs), nitrogen dioxide (NO2), carbon monoxide (CO), and particulate matter with an aerodynamic diameter of 2.5 microns or less (PM2.5). To predict air quality, the device was deployed from November 1, 2022, to February 4, 2023, in certain bauxite-rich areas of Adamawa and certain volcanic sites in western Cameroon. Therefore, machine learning algorithm models, namely, multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), XGBoost (XGB), and K-nearest neighbors (KNN) were applied to analyze the collected concentrations and predict the future state of air quality. The performance of these models was evaluated using mean absolute error (MAE), coefficient of determination (R-square), and root mean square error (RMSE). The obtained data in this study show that these pollutants are present in selected localities albeit to different extents. Moreover, the AQI values obtained range from 10 to 530, with a mean of 132.380 ± 63.705, corresponding to moderate air quality state but may induce an adverse effect on sensitive members of the population. This study revealed that XGB regression performed better in air quality forecasting with the highest R-squared (test score of 0.9991 and train score of 0.9999) and lowest RMSE (test score of 1.5748 and train score of 0. 0073) and MAE (test score of 0.0872 and train score of 0.0020), while the KNN model had the worst prediction (lowest R-squared and highest RMSE and MAE). This embryonic work is a prototype for projects in Cameroon as measurements are underway for a national spread over a longer period of time.

First-time calculation of the spatial distribution of concentration and air quality index over South Africa using TROPOMI data.

The release of toxic gases into the atmosphere may reach concentrations that can cause undesirable health, economic, or aesthetic effects. It is therefore important to monitor the amounts of pollutants injected into the atmosphere from various sources. Most countries have a ground network with multiple measuring sites and instruments, that can measure the air quality index (AQI). However, the main challenge with the networks is the low spatial coverage. In this work, satellite data is used to calculate for the first time the spatial distribution of AQI and pollutant concentration over South Africa. The TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P data is used to calculate AQI from carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2) gases. The results that the month of June has the worst air quality distribution throughout the country, while March has the best air quality distribution. Overall, the results clearly show that TROPOMI has the capability to measure air quality at a country and city level.Implications: In this work, satellite data is used to calculate for the first time the spatial distribution of the air quality index (AQI) and pollutant concentration over South Africa. The TROPOspheric Monitoring Instrument (TROPOMI) onboard Sentinel-5P data is used to calculate AQI from carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2) gases. Currently, South Africa has a ground network of instruments that measure AQ, however, the network does not cover the whole country. In this work, we show that the use of TROPOMI can compliment the current network and provide data for the areas not covered.

Development and Validation of Low-Cost Indoor Air Quality Monitoring System for Swine Buildings.

The optimal indoor environment is associated with comfortable temperatures along with favorable indoor air quality. One of the air pollutants, particulate matter (PM), is potentially harmful to animals and humans. Most farms have monitoring systems to identify other hazardous gases rather than PM due to the sensor cost. In recent decades, the application of environmental monitoring systems based on Internet of Things (IoT) devices that incorporate low-cost sensors has elevated extensively. The current study develops a low-cost air quality monitoring system for swine buildings based on Raspberry Pi single-board computers along with a sensor array. The system collects data using 11 types of environmental variables along with temperature, humidity, CO2, light, pressure, and different types of gases, namely PM1, PM2.5, and PM10. The system is designed with a central web server that provides real-time data visualization and data availability through the Internet. It was tested in actual pig barns to ensure stability and functionality. In addition, there was a collocation test conducted by placing the system in two different pig barns to validate the sensor data. The Wilcoxon rank sum test demonstrates that there are no significant differences between the two sensor datasets, as all variables have a p-value greater than 0.05. However, except for carbon monoxide (CO), none of the variables exhibit correlation exceeding 0.5 with PM concentrations. Overall, a scalable, portable, non-complex, low-cost air quality monitoring system was successfully developed within a cost of USD 94.

Quantifying CO-release from a photo-CORM using 19F NMR: An investigation into light-induced CO delivery.

Carbon monoxide (CO) is an innate signaling molecule that can regulate immune responses and interact with crucial elements of the circadian clock. Moreover, pharmacologically, CO has been substantiated for its therapeutic advantages in animal models of diverse pathological conditions. Given that an excessive level of CO can be toxic, it is imperative to quantify the necessary amount for therapeutic use accurately. However, estimating gaseous CO is notably challenging. Therefore, novel techniques are essential to quantify CO in therapeutic applications and overcome this obstacle precisely. The classical Myoglobin (Mb) assay technique has been extensively used to determine the amount of CO-release from CO-releasing molecules (CORMs) within therapeutic contexts. Nevertheless, specific challenges arise when applying the Mb assay to evaluate CORMs featuring innovative molecular architectures. Here, we report a fluorinated photo-CORM (CORM-FBS) for the photo-induced CO-release. We employed the 19F NMR spectroscopy approach to monitor the release of CO as well as quantitative evaluation of CO release. This new 19F NMR approach opens immense opportunities for researchers to develop reliable techniques for identifying molecular structures, quantitative studies of drug metabolism, and monitoring the reaction process.

Ambient air pollution exposure and COVID-19 related hospitalizations in Santiago, Chile.

Morbidity and mortality from several diseases are increased on days of higher ambient air pollution. We carried out a daily time-series analysis with distributive lags to study the influence of short-term air pollution exposure on COVID-19 related hospitalization in Santiago, Chile between March 16 and August 31, 2020. Analyses were adjusted for temporal trends, ambient temperature, and relative humidity, and stratified by age and sex. 26,579 COVID-19 hospitalizations were recorded of which 24,501 were laboratory confirmed. The cumulative percent change in hospitalizations (95% confidence intervals) for an interquartile range increase in air pollutants were: 1.1 (0.2, 2.0) for carbon monoxide (CO), 0.30 (0.0, 0.50) for nitrogen dioxide (NO2), and 2.7 (1.9, 3.0) for particulate matter of diameter ≤ 2.5 microns (PM2.5). Associations with ozone (O3), particulate matter of diameter ≤ 10 microns (PM10) and sulfur dioxide (SO2) were not significant. The observed effect of PM2.5 was significantly greater for females and for those individuals ≥ 65 years old. This study provides evidence that daily increases in air pollution, especially PM2.5, result in a higher observed risk of hospitalization from COVID-19. Females and the elderly may be disproportionately affected.

Recent decline in carbon monoxide levels observed at an urban site in Ahmedabad, India.

Carbon monoxide (CO) is a prominent air pollutant in cities, with far-reaching implications for both local air quality and global atmospheric chemistry. The long-term change in atmospheric CO levels at a specific location is influenced by a complex interplay of local emissions, atmospheric transport, and photochemical processes, making it a subject of considerable interest. This study presents an 8-year analysis (2014-2021) of in situ CO observations using a cutting-edge laser-based analyzer at an urban site in Ahmedabad, western India. The long-term observations reveal a subtle trend in CO levels, masked by contrasting year-to-year variations, particular after 2018, across distinct diurnal time windows. Mid-afternoon (12:00-16:00 h) CO levels, reflecting background and regional conditions, remained relatively stable over the study period. In contrast, evening (18:00-21:00 h) CO levels, influenced by local emissions, exhibited substantial inter-annual variability without discernible trends from 2014 to 2018. However, post-2018, evening CO levels showed a consistent decline, predating COVID-19 lockdown measures. This decline coincided with the nationwide adoption of Bharat stage IV emission standards and other measures aimed at reducing vehicular emissions. The COVID-19 lockdown in 2020 further resulted in a noteworthy 29% reduction in evening CO levels compared to the pre-lockdown (2014-2019) period, highlighting the potential for substantial CO reduction through stringent vehicular emission controls. The observed long-term changes in CO levels do not align with the decreasing emission estimated by various inventories from 2014 to 2018, suggesting a need for improved emission statistics in Indian urban regions. This study underscores the importance of ongoing continuous CO measurements in urban areas to inform policy efforts aimed at controlling atmospheric pollutants.

Air quality and kidney health: Assessing the effects of PM10, PM2.5, CO, and NO2 on renal function in primary glomerulonephritis.

BACKGROUND: While extensive studies have elucidated the relationships between exposure to air pollution and chronic diseases, such as cardiovascular disorders and diabetes, the intricate effects on specific kidney diseases, notably primary glomerulonephritis (GN)-an immune-mediated kidney ailment-are less well understood. Considering the escalating incidence of GN and conspicuous lack of investigative focus on its association with air quality, investigation is dedicated to examining the long-term effects of air pollutants on renal function in individuals diagnosed with primary GN. METHODS: This retrospective cohort analysis was conducted on 1394 primary GN patients who were diagnosed at Seoul National University Bundang Hospital and Seoul National University Hospital. Utilizing time-varying Cox regression and linear mixed models (LMM), we examined the effect of yearly average air pollution levels on renal function deterioration (RFD) and change in estimated glomerular filtration rate (eGFR). In this context, RFD is defined as sustained eGFR of less than 60 mL/min per 1.73 m2. RESULTS: During a mean observation period of 5.1 years, 350 participants developed RFD. Significantly, elevated interquartile range (IQR) levels of air pollutants-including PM10 (particles ≤10 micrometers, HR 1.389, 95 % CI 1.2-1.606), PM2.5 (particles ≤2.5 micrometers, HR 1.353, 95 % CI 1.162-1.575), CO (carbon monoxide, HR 1.264, 95 % CI 1.102-1.451), and NO2 (nitrogen dioxide, HR 1.179, 95 % CI 1.021-1.361)-were significantly associated with an increased risk of RFD, after factoring in demographic and health variables. Moreover, exposure to PM10 and PM2.5 was associated with decreased eGFR. CONCLUSIONS: This study demonstrates a substantial link between air pollution exposure and renal function impairment in primary GN, accentuating the significance of environmental determinants in the pathology of immune-mediated kidney diseases.

The short-term effects of individual and mixed ambient air pollutants on suicide mortality: A case-crossover study.

It is critical to explore intervenable environmental factors in suicide mortality. Based on 30,688 suicide cases obtained from the Mortality Surveillance System of the Jiangsu Provincial Centre for Disease Control and Prevention, we utilized a case-crossover design, and found that the OR of suicide deaths increased by a maximum of 0.71 % (95 % CI: 0.09 %, 1.32 %), 0.68 % (95 % CI: 0.12 %, 1.25 %), 0.77 % (95 % CI: 0.19 %, 1.37 %), 2.95 % (95 % CI: 1.62 %, 4.29 %), 4.18 % (95 % CI: 1.55 %, 6.88 %), and 0.93 % (95 % CI: 0.10 %, 1.77 %), respectively, for per 10 µg/m3 increase in the particulate matter (PM) with diameters ≤ 2.5 µm (PM2.5), PM with diameters ≤ 10 µm (PM10), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and per 0.1 mg/m3 increase in carbon monoxide (CO) concentrations with the conditional logistic regression analysis. People living in county-level cities were more susceptible. Particularly, a significant positive association was found between air pollutant mixture exposure and suicide deaths (OR=1.04,95 % CI: 1.01, 1.06). The excess fraction of suicide deaths due to air pollution reached a maximum of 8.07 %. In conclusion, we found associations between individual and mixed ambient air pollutants and suicide deaths, informing the development of integrated air pollution management and targeted measures for suicide prevention and intervention. ENVIRONMENTAL IMPLICATION: As a major contributor to the global burden of disease, air pollution was confirmed by accumulating studies to have adverse impact on mental health, and potentially lead to suicide deaths. However, systematic studies on the association between air pollution and suicide mortality are lacking. We explored the associations of multiple air pollutants and pollution mixtures with suicide deaths and assessed excess suicide mortality due to air pollution, emphasizing the importance of air pollution control on suicide prevention. Our study provides evidence to support mechanistic studies on the association between air pollution and suicide, and informs comprehensive air pollution management.

Association between risk of preterm birth and long-term and short-term exposure to ambient carbon monoxide during pregnancy in chongqing, China: a study from 2016-2020.

BACKGROUND: Preterm birth (PTB) is an important predictor of perinatal morbidity and mortality. Previous researches have reported a correlation between air pollution and an increased risk of preterm birth. However, the specific relationship between short-term and long-term exposure to carbon monoxide (CO) and preterm birth remains less explored. METHODS: A population-based study was conducted among 515,498 pregnant women in Chongqing, China, to assess short-term and long-term effects of CO on preterm and very preterm births. Generalized additive models (GAM) were applied to evaluate short-term effects, and exposure-response correlation curves were plotted after adjusting for confounding factors. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated using COX proportional hazard models to estimate the long-term effect. RESULTS: The daily incidence of preterm and very preterm birth was 5.99% and 0.41%, respectively. A positive association between a 100 µg/m³ increase in CO and PTB was observed at lag 0-3 days and 12-21 days, with a maximum relative risk (RR) of 1.021(95%CI: 1.001-1.043). The exposure-response curves (lag 0 day) revealed a rapid increase in PTB due to CO. Regarding long-term exposure, positive associations were found between a 100 µg/m3 CO increase for each trimester(Model 2 for trimester 1: HR = 1.054, 95%CI: 1.048-1.060; Model 2 for trimester 2: HR = 1.066, 95%CI: 1.060-1.073; Model 2 for trimester 3: HR = 1.007, 95%CI: 1.001-1.013; Model 2 for entire pregnancy: HR = 1.080, 95%CI: 1.073-1.088) and higher HRs of very preterm birth. Multiplicative interactions between air pollution and CO on the risk of preterm and very preterm birth were detected (P- interaction<0.05). CONCLUSIONS: Our findings suggest that short-term exposure to low levels of CO may have protective effects against preterm birth, while long-term exposure to low concentrations of CO may reduce the risk of both preterm and very preterm birth. Moreover, our study indicated that very preterm birth is more susceptible to the influence of long-term exposure to CO during pregnancy, with acute CO exposure exhibiting a greater impact on preterm birth. It is imperative for pregnant women to minimize exposure to ambient air pollutants.