Amazonia as a carbon source linked to deforestation and climate change.

Amazonia hosts the Earth's largest tropical forests and has been shown to be an important carbon sink over recent decades1-3. This carbon sink seems to be in decline, however, as a result of factors such as deforestation and climate change1-3. Here we investigate Amazonia's carbon budget and the main drivers responsible for its change into a carbon source. We performed 590 aircraft vertical profiling measurements of lower-tropospheric concentrations of carbon dioxide and carbon monoxide at four sites in Amazonia from 2010 to 20184. We find that total carbon emissions are greater in eastern Amazonia than in the western part, mostly as a result of spatial differences in carbon-monoxide-derived fire emissions. Southeastern Amazonia, in particular, acts as a net carbon source (total carbon flux minus fire emissions) to the atmosphere. Over the past 40 years, eastern Amazonia has been subjected to more deforestation, warming and moisture stress than the western part, especially during the dry season, with the southeast experiencing the strongest trends5-9. We explore the effect of climate change and deforestation trends on carbon emissions at our study sites, and find that the intensification of the dry season and an increase in deforestation seem to promote ecosystem stress, increase in fire occurrence, and higher carbon emissions in the eastern Amazon. This is in line with recent studies that indicate an increase in tree mortality and a reduction in photosynthesis as a result of climatic changes across Amazonia1,10.

Vast CO2 release from Australian fires in 2019-2020 constrained by satellite.

Southeast Australia experienced intensive and geographically extensive wildfires during the 2019-2020 summer season1,2. The fires released substantial amounts of carbon dioxide into the atmosphere3. However, existing emission estimates based on fire inventories are uncertain4, and vary by up to a factor of four for this event. Here we constrain emission estimates with the help of satellite observations of carbon monoxide5, an analytical Bayesian inversion6 and observed ratios between emitted carbon dioxide and carbon monoxide7. We estimate emissions of carbon dioxide to be 715 teragrams (range 517-867) from November 2019 to January 2020. This is more than twice the estimate derived by five different fire inventories8-12, and broadly consistent with estimates based on a bottom-up bootstrap analysis of this fire episode13. Although fires occur regularly in the savannas in northern Australia, the recent episodes were extremely large in scale and intensity, burning unusually large areas of eucalyptus forest in the southeast13. The fires were driven partly by climate change14,15, making better-constrained emission estimates particularly important. This is because the build-up of atmospheric carbon dioxide may become increasingly dependent on fire-driven climate-carbon feedbacks, as highlighted by this event16.

Xanthene-Based Dyes for Photoacoustic Imaging and their Use as Analyte-Responsive Probes.

Developing imaging tools that can report on the presence of disease-relevant analytes in multicellular organisms can provide insight into fundamental disease mechanisms as well as provide diagnostic tools for the clinic. Photoacoustic imaging (PAI) is a light-in, sound-out imaging technique that allows for high resolution, deep-tissue imaging with applications in pre-clinical and point-of-care settings. The continued development of near-infrared (NIR) absorbing small-molecule dyes promises to improve the capabilities of this emerging imaging modality. For example, new dye scaffolds bearing chemoselective functionalities are enabling the detection and quantification of disease-relevant analytes through activity-based sensing (ABS) approaches. Recently described strategies to engineer NIR absorbing xanthenes have enabled development of analyte-responsive PAI probes using this classic dye scaffold. Herein, we present current strategies for red-shifting the spectral properties of xanthenes via bridging heteroatom or auxochrome modifications. Additionally, we explore how these strategies, coupled with chemoselective spiroring-opening approaches, have been employed to create ABS probes for in vivo detection of hypochlorous acid, nitric oxide, copper (II), human NAD(P)H: quinone oxidoreductase isozyme 1, and carbon monoxide. Given the versatility of the xanthene scaffold, we anticipate continued growth and development of analyte-responsive PAI imaging probes based on this dye class.

A scalar-on-quantile-function approach for estimating short-term health effects of environmental exposures.

Environmental epidemiologic studies routinely utilize aggregate health outcomes to estimate effects of short-term (eg, daily) exposures that are available at increasingly fine spatial resolutions. However, areal averages are typically used to derive population-level exposure, which cannot capture the spatial variation and individual heterogeneity in exposures that may occur within the spatial and temporal unit of interest (eg, within a day or ZIP code). We propose a general modeling approach to incorporate within-unit exposure heterogeneity in health analyses via exposure quantile functions. Furthermore, by viewing the exposure quantile function as a functional covariate, our approach provides additional flexibility in characterizing associations at different quantile levels. We apply the proposed approach to an analysis of air pollution and emergency department (ED) visits in Atlanta over 4 years. The analysis utilizes daily ZIP code-level distributions of personal exposures to 4 traffic-related ambient air pollutants simulated from the Stochastic Human Exposure and Dose Simulator. Our analyses find that effects of carbon monoxide on respiratory and cardiovascular disease ED visits are more pronounced with changes in lower quantiles of the population's exposure. Software for implement is provided in the R package nbRegQF.

Biomarkers of Waterpipe Tobacco Smoke Exposure: A Systematic Review and Meta-Analysis.

INTRODUCTION: The prevalence of waterpipe tobacco smoking is increasing globally. Biomarkers of waterpipe tobacco smoke (WTS) exposure are less studied. AIMS AND METHODS: To identify the types of biomarkers of WTS exposure and estimate changes in biomarker concentrations pre- to post-WTS exposure. PubMed, Embase, Web of Science, CINAHL Plus, PsycINFO, and Cochrane Library were searched for studies up to April 24, 2023. The types of biomarkers were identified. Random-effects models were used to estimate changes in biomarker concentrations pre- to post-WTS exposure. RESULTS: Seventy-three studies involving 3755 participants exposed to WTS (49% male, mean age: 24.8 years) and 11 types of biomarkers of WTS exposure were identified. The biomarkers included tobacco alkaloids, expired carbon monoxide (eCO), carboxyhemoglobin (COHb), tobacco-specific nitrosamines, volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs), heavy metals, unmetabolized VOCs, unmetabolized PAHs, furan metabolites, and heterocyclic aromatic amines. Compared with pre-WTS exposure, eCO (breath; mean difference [MD] 27.00 ppm; 95% confidence interval [CI]: 20.91 to 33.08), COHb (blood; MD 4.30%; 95%CI: 2.57 to 6.03), COHb (breath; MD 7.14%; 95%CI: 4.96 to 9.31), nicotine (blood; MD 8.23 ng/mL; 95%CI: 6.27 to 10.19), and cotinine (urine; MD 110.40 ng/mL; 95%CI: 46.26 to 174.54) significantly increased post-WTS exposure. CONCLUSIONS: Biomarkers of WTS exposure were systematically identified. The similarity between the biomarkers of WTS exposure and those of cigarette smoke and higher concentrations of some biomarkers post-WTS exposure underscore the need for further research on applying biomarkers in surveillance, interventions, and regulations to mitigate the harms of waterpipe tobacco smoking. IMPLICATIONS: This study provides the first comprehensive overview of biomarkers investigated and available for assessing WTS exposure and their concentration changes in the human body. Researchers can use biomarkers such as eCO, COHb, nicotine, and cotinine to measure the health risks associated with WTS exposure and objectively evaluate the effectiveness of public health interventions aimed at reducing waterpipe tobacco smoking. Public health policymaking can also be informed through increased biomarker concentrations following WTS exposure, to implement regulations and public health education campaigns on limiting or preventing waterpipe tobacco smoking.

Biodiesel synthesis from chicken feather meal using S/AlMCM-41 catalyst and engine performance analysis.

In this study, Sulphated/AlMCM-41 (S/AlMCM-41) catalysts were synthesized and used to produce biodiesel from CFMO. Different percentages of S/AlMCM-41 catalysts were prepared and characterized by X-ray diffraction, BET studies, TPD, and SEM-EDS analysis. Sulphur incorporation to the MCM framework though reduced the surface area, and pore volume of the catalyst, sufficient acidity were produced in the catalyst surface. The existence of functional groups and the composition of the biodiesel obtained was analysed by FTIR and GC-MS. S/AlMCM-41 (80%) catalyst presented a high catalytic activity with maximum biodiesel conversion % when compared to other variants. The bio-ester produced from CFMO with S/AlMCM-41 (80%) catalyst possessed the higher calorific value of 50 MJ/kg and flashpoint of 153 °C and other properties analogous to the standard biodiesel. The engine performance was examined for biodiesel blends with neat diesel, where biodiesel blends performed better than neat diesel. The exhaust gas emission studies also highlighted that the obtained biodiesel showed emission characteristics similar to the standard biodiesel, whereas marginally higher emission for CO and CO2 of about 2.2 and 7.9% was reported.

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.

For Submission to the Journal of Community Health: Carbon Monoxide Awareness and Detector Use in the State of Wisconsin.

Carbon monoxide (CO) is a leading cause of poisoning. CO detectors are a known-effective prevention strategy, however, little is known about use of detectors or knowledge of risk. This study assessed awareness of CO poisoning risk, detector laws, and detector use among a statewide sample. Data collected from the Survey of the Health of Wisconsin (SHOW) included a CO Monitoring module added to the in-home interview for 466 participants representing unique households across Wisconsin in 2018-2019. Univariate and multivariable logistic regression models examined associations between demographic characteristics, awareness of CO laws and detector use. Less than half of households had a verified CO detector. Under 46% were aware of the detector law. Those aware had 2.82 greater odds of having a detector in the home compared to those unaware of the law. Lack of CO law awareness may lead to less frequent detector use and result in higher risk of CO poisoning. This highlights the need for CO risk and detector education to decrease poisonings.

Scalable deep learning to identify brick kilns and aid regulatory capacity.

Improving compliance with environmental regulations is critical for promoting clean environments and healthy populations. In South Asia, brick manufacturing is a major source of pollution but is dominated by small-scale, informal producers who are difficult to monitor and regulate-a common challenge in low-income settings. We demonstrate a low-cost, scalable approach for locating brick kilns in high-resolution satellite imagery from Bangladesh. Our approach identifies kilns with 94.2% accuracy and 88.7% precision and extracts the precise GPS coordinates of every brick kiln across Bangladesh. Using these estimates, we show that at least 12% of the population of Bangladesh (>18 million people) live within 1 km of a kiln and that 77% and 9% of kilns are (illegally) within 1 km of schools and health facilities, respectively. Finally, we show how kilns contribute up to 20.4 µg/[Formula: see text] of [Formula: see text] (particulate matter of a diameter less than 2.5 µm) in Dhaka when the wind blows from an unfavorable direction. We document inaccuracies and potential bias with respect to local regulations in the government data. Our approach demonstrates how machine learning and Earth observation can be combined to better understand the extent and implications of regulatory compliance in informal industry.

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.

Experimental assessment of pollutant emissions from residential fuel cells and comparative benchmark analysis.

Energy transition currently brings focus on fuel cell micro-combined heat and power (mCHP) systems for residential uses. The two main technologies already commercialized are the Proton Exchange Membrane Fuel Cells (PEMFCs) and Solid Oxide Fuel Cells (SOFCs). The pollutant emissions of one system of each technology have been tested with a portable probe both in laboratory and field-test configurations. In this paper, the nitrogen oxides (NOx), sulphur dioxide (SO2), and carbon monoxide (CO) emission levels are compared to other combustion technologies such as a recent Euro 6 diesel automotive vehicle, a classical gas condensing boiler, and a gas absorption heat pump. At last, a method of converting the concentration of pollutants (in ppm) measured by the sensors into pollutant intensity per unit of energy (in mg/kWh) is documented and reported. This allows for comparing the pollutant emissions levels with relevant literature, especially other studies conducted with other measuring sensors. Both tested residential fuel cell technologies fed by natural gas can be considered clean regarding SO2 and NOx emissions. The CO emissions can be considered quite low for the tested SOFC and even nil for the tested PEMFC. The biggest issue of natural gas fuel cell technologies still lies in the carbon dioxide (CO2) emissions associated with the fossil fuel they consume. The gas absorption heat pump however shows worse NOx and CO levels than the classical gas condensing boiler. At last, this study illustrates that the high level of hybridization between a fuel cell and a gas boiler may be responsible for unexpected ON/OFF cycling behaviours and therefore prevent both sub-systems from operating as optimally and reliably as they would have as standalone units.

A Field Study of Carbon Monoxide Levels in Snow Caves During Short-Term Stove Use.

INTRODUCTION: While the use of camping stoves in poorly ventilated areas is discouraged, the need to address dehydration challenges in harsh arctic conditions has led to their unconventional use inside snow caves for snow melting, subjecting occupants to unknown carbon monoxide (CO) levels. This study, located at sea level in northeastern Greenland, aimed to assess CO levels and dynamics during short cooking sessions in newly constructed emergency snow caves. METHODS: In 5 snow caves, constructed according to the same design principles by 4 different individuals, a single MSR Whisperlite multifuel burner, primed with ethanol and burning white gas, was used to melt snow. CO concentrations were monitored every minute until all the snow in a 5-L pot was converted to water and CO levels returned to below 10 ppm. RESULTS: A total of 16 experiments conducted showed that the priming phase generated the highest CO peaks, with a maximum of 120 ppm. Time-weighted averages ranged from 14 ppm to 67 ppm, with trial durations of 15 to 21 min. A single trial with a dirty burner resulted in up to a 10-fold increase in CO levels. CONCLUSIONS: While single, short cooking sessions of less than 10 min burn time in newly constructed snow caves may be tolerated under specific conditions, the study highlighted substantial variation between caves and the importance of using clean burners, emphasizing the need for further research to gain a comprehensive understanding of CO exposure dynamics in snow caves.

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.

Assessing Tunisia's urban air quality using combined lichens and Sentinel-5 satellite integration.

In Tunisia, urban air pollution is becoming a bigger problem. This study used a combined strategy of biomonitoring with lichens and satellite mapping with Sentinel-5 satellite data processed in Google Earth Engine (GEE) to assess the air quality over metropolitan Tunis. Lichen diversity was surveyed across the green spaces of the Faculty of Science of Tunisia sites, revealing 15 species with a predominance of pollution-tolerant genera. The Index of Atmospheric Purity (IAP) calculated from the lichen data indicated poor air quality. Spatial patterns of pollutants sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and aerosol index across Greater Tunis were analyzed from Sentinel-5 datasets on the GEE platform. The higher values of these indices in the research area indicate that it may be impacted by industrial activity and highlight the considerable role that vehicle traffic plays in air pollution. The results of the IAP, IBL, and the combined ground-based biomonitoring and satellite mapping techniques confirm poor air quality and an environment affected by atmospheric pollutants which will enable proactive air quality management strategies to be put in place in Tunisia's rapidly expanding cities.

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.

Urban air pollution evaluation in downtown streets of a medium-sized Latin American city using AERMOD dispersion model.

The transport sector is considered the largest contributor of air pollutants in urban areas, mainly on-road vehicles, affecting the environment and human health. Bahía Blanca is a medium-sized Latin American city, with high levels of traffic in the downtown area during peak hours. In this regard, it is necessary to analyze air pollution using an air quality model considering that there are no air pollutant measurements in the central area. Furthermore, this type of study has not been carried out in the region and since the city is expected to grow, it is necessary to evaluate the current situation in order to make effective future decisions. In this sense, the AERMOD model (US-EPA version) and the RLINE source type were used in this work. This study analyzes the variations of pollutant concentrations coming from mobile sources in Bahía Blanca's downtown area, particularly carbon monoxide (CO) and nitrogen oxides (NOx) during the period Jul-2020 to Jun-2022. It is interesting to note the results show the maximum concentration values detected are not directly associated with maximum levels of vehicle flow or emission rates, which highlights the importance of meteorological parameters in the modeling. In addition, alternative scenarios are proposed and analyzed from a sustainable approach. Regarding the scenario analysis, it can be concluded that diesel vehicles have a large influence on NOx emissions. Moreover, restrictions as strict as those proposed for a Low Emission Zone would be less applicable in the city than alternative temporary measures that modify traffic at peak hours.

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.

Trans-boundary spatio-temporal analysis of Sentinel 5P tropospheric nitrogen dioxide and total carbon monoxide columns over Punjab and Haryana Regions with COVID-19 lockdown impact.

This study conducts a spatio-temporal analysis of tropospheric nitrogen dioxide (NO2) and total carbon monoxide (CO) concentrations in the Punjab and Haryana regions of India and Pakistan, using datasets from the Sentinel 5-Precursor (S5P) satellite. These regions, marked by diverse economic growth factors including population expansion, power generation, transportation, and agricultural practices, face similar challenges in atmospheric pollution, particularly evident in major urban centers like Delhi and Lahore, identified as pollution hotspots. The study also spotlights pollution associated with power plants. In urban areas, tropospheric NO2 levels are predominantly elevated due to vehicular emissions, whereas residential activities mainly contribute to CO pollution. However, precisely attributing urban CO sources is complex due to its longer atmospheric residence time and intricate circulation patterns. Notably, the burning of rice crop residue in November significantly exacerbates winter pollution episodes and smog, showing a more pronounced correlation with total CO than with tropospheric NO2 levels. The temporal analysis indicates that the months from October to December witness peak pollution, contrasted with the relatively cleaner period during the monsoon months of July to September. The severe pollution in the OND quarter is attributed to factors such as variations in boundary layer height and depletion of OH radicals. Furthermore, the study highlights the positive impact of the COVID-19 lockdown on air quality, with a significant decrease in NO2 concentrations during April, 2020 (Delhi: 59%, Lahore: 58%). However, the reduction in total CO columns was less significant. The study also correlates lockdown stringency with tropospheric NO2 columns (R2: 0.37 for Delhi, 0.25 for Lahore, 0.22 for Rawalpindi/Islamabad), acknowledging the influence of various meteorological and atmospheric variables. The research highlights the significant impact of crop residue burning on winter pollution levels, particularly on total CO concentrations. The study also shows the notable effect of the COVID-19 lockdown on air quality, significantly reducing NO2 levels. Additionally, it explores the correlation between lockdown stringency and tropospheric NO2 columns, considering various meteorological factors.

Hourly Air Pollutants and Acute Coronary Syndrome Onset in 1.29 Million Patients.

BACKGROUND: Short-term exposure to ambient air pollution has been linked with daily hospitalization and mortality from acute coronary syndrome (ACS); however, the associations of subdaily (hourly) levels of criteria air pollutants with the onset of ACS and its subtypes have rarely been evaluated. METHODS: We conducted a time-stratified case-crossover study among 1 292 880 patients with ACS from 2239 hospitals in 318 Chinese cities between January 1, 2015, and September 30, 2020. Hourly concentrations of fine particulate matter (PM2.5), coarse particulate matter (PM2.5-10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), and ozone (O3) were collected. Hourly onset data of ACS and its subtypes, including ST-segment-elevation myocardial infarction, non-ST-segment-elevation myocardial infarction, and unstable angina, were also obtained. Conditional logistic regressions combined with polynomial distributed lag models were applied. RESULTS: Acute exposures to PM2.5, NO2, SO2, and CO were each associated with the onset of ACS and its subtypes. These associations were strongest in the concurrent hour of exposure and were attenuated thereafter, with the weakest effects observed after 15 to 29 hours. There were no apparent thresholds in the concentration-response curves. An interquartile range increase in concentrations of PM2.5 (36.0 µg/m3), NO2 (29.0 µg/m3), SO2 (9.0 µg/m3), and CO (0.6 mg/m3) over the 0 to 24 hours before onset was significantly associated with 1.32%, 3.89%, 0.67%, and 1.55% higher risks of ACS onset, respectively. For a given pollutant, the associations were comparable in magnitude across different subtypes of ACS. NO2 showed the strongest associations with all 3 subtypes, followed by PM2.5, CO, and SO2. Greater magnitude of associations was observed among patients older than 65 years and in the cold season. Null associations of exposure to either PM2.5-10 or O3 with ACS onset were observed. CONCLUSIONS: The results suggest that transient exposure to the air pollutants PM2.5, NO2, SO2, or CO, but not PM2.5-10 or O3, may trigger the onset of ACS, even at concentrations below the World Health Organization air quality guidelines.

Carbon monoxide as a clinical marker of hemolysis.

Carbon monoxide (CO)-based tests have precisely measured hemolysis for over 40 years. End-tidal CO was the primary marker in clinical hematology research, followed by carboxyhemoglobin. Quantification of CO reflects heme oxygenases degrading heme in a 1:1 stoichiometric ratio, making CO a direct marker of hemolysis. CO in alveolar air can be quantified using gas chromatography, whose high resolution allows detecting mild and moderate levels of hemolysis. CO can be elevated in active bleeding, resorbing hematoma, and smoking. Clinical acumen and other markers remain necessary to diagnose the cause of hemolysis. CO-based tests constitute an opportunity for bench-to-bedside technology transfer.