Metallic catalyst loaded 2D perovskite sheets for heightened surface activity for selective chemiresistive sensing

This research has designed a unique method of simulating the movement of air pollutants with respect to winds direction in urban areas. A good study of variable and invariable parameters for the factors of air emissions in urban area has assisted in conceptualization of major forces and factors that are responsible for the dispersion of air pollution in urban areas. These parameters have been used in the design, development and simulation of this model. New findings include, establishment of method for tracing the direction of air emissions in urban areas using trigonometric and vector analysis. The model was designed on the basis of variable and invariable parameters for the factors influencing air pollution in urban areas. Good choice of constraints was made. The Model has quantitatively established the mathematical and physical relationship between the air concentration and deposition values of one or more pollution species in space and time. It uses atmospheric, and meteorological variables and parameters, to describe removal and transfonnation processes in urban areas. The fimdamental approach and empirical analysis of various factors governing the distribution of the air pollutants in urban areas gave new results and findings. Results are discussed.

Brain Fog: Does Air Pollution Make Us Less Productive?

Mosses are useful, ubiquitous accumulation biomonitors and as such can be used for biomonitoring surveys. However, the biomonitoring of atmospheric pollution can be compromised in urban contexts if the targeted biomonitors are regularly disturbed, irregularly distributed, or are difficult to access. Here, we test the hypothesis that cemeteries are appropriate moss sampling sites for the evaluation of air pollution in urban areas. We sampled mosses growing on gravestones in 21 urban and peri-urban cemeteries in the Paris metropolitan area. We focused on Grimmia pulvinata (Hedwig) Smith, a species abundantly found in all studied cemeteries and very common in Europe. The concentration of Al, As, Br, Ca, Ce, Cl, Cr, Cu, Fe, K, Mn, Ni, V, P, Pb, Rb, S, Sr, Ti, and Zn was determined by a total reflection X-ray fluorescence technique coupled with a slurry sampling method (slurry-TXRF). This method avoids a digestion step, reduces the risk of sample contamination, and works even at low sample quantities. Elemental markers of road traffic indicated that the highest polluted cemeteries were located near the highly frequented Parisian ring road and under the influence of prevailing winds. The sites with the lowest pollution were found not only in the peri-urban cemeteries, adjoining forest or farming landscapes, but also in the large and relatively wooded cemeteries located in the center of Paris. Our results suggest that (1) slurry-TXRF might be successfully used with moss material, (2) G. pulvinata might be a good biomonitor of trace metals air pollution in urban context, and (3) cemetery moss sampling could be a useful complement for monitoring urban areas. Graphical abstract We tested the hypothesis that cemeteries are appropriate moss sampling sites for the evaluation of air pollution in urban areas. We sampled 110 moss cushions (Grimmia pulvinata) growing on gravestones in 21 urban and peri-urban cemeteries in the Paris metropolitan area. The concentration of 20 elements in mosses was determined by a total reflection X-ray fluorescence technique coupled with a slurry sampling method. Statistical analysis revealed that: - Urbanized Parisian areas crossed by traffic roads have the highest polluted cemeteries with a strong influence of main wind direction on the distribution of air pollutants - As expected, small cemeteries with low tree density were heavily polluted - Less obvious, large green spaces such as large cemeteries (Père Lachaise, Montmartre, Montparnasse) in the center of a dense metropolis like Paris present the same level of atmospheric trace metal pollution as cemeteries in less urbanized areas or nearing a very large forest. This suggests that even in densely urbanized areas, there is more spatial variability in pollution distribution than usually assumed and that large urban areas with low traffic and green filters such as trees are likely to intercept air pollutants.

Air pollution is an important environmental issue prevailing in the urban landscape of Delhi, the capital city of India which is the 2nd most populous city and most polluted city in the world. The air pollution and land surface temperature (LST) are the most prominent environmental issues in the urban areas. However, there is no comprehensive analysis of the relationship between LST and air pollutants. The objective of the present study is to investigate: (a) the spatio-temporal distribution of LST (diurnal) and the criteria pollutants of carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particle matter (PM2.5, PM10) and Sulphur dioxide (SO2) and (b) the relationship between LST and air pollutants concentration during different seasons in Delhi and its surroundings during 2003–2023. In this study, Landsat satellite data (ETM and OLI) and MODIS satellite data are used to extract the LST for the period 2003-2023. The satellite-based air pollution data is obtained from TROPOMI and MODIS, and ground-based data are obtained from the Central Pollution Control Board (CPCB) monitoring station. Our result reveals that in the spatio-temporal analysis, the LST has increased significantly while the air pollution increased (decreased) over the period. It is observed that there is a strong relationship between LST and air pollutants during winter among all seasons. SO2 has a significant correlation with LST (R2 = 0.74). Additionally, PM2.5 and PM10 are identified as the main air pollutants affecting LST variations during the winter season (R2 = 0.59 to 0.64). Our results conclude that variation in the LST is not only dependent on its surface properties but also on the associated meteorological conditions. The findings of this study have significant implications for future scientific research as this study provides the integration of effective mitigation strategies to combat the challenges of increasing LST and air pollution in urban areas.

Air pollution in urban areas has become a serious public health problem. Public health impacts can include acute and chronic respiratory diseases, cardiovascular diseases, lung cancer, and even death. This study aims to analyze the impact of air pollution on urban public health. The method used in this study is a literature review by examining various sources of data, such as scientific journals, government reports, and health statistics. data. The results show that air pollution has a significant impact on the health of urban communities, especially for vulnerable groups such as children, the elderly and those with the poorest health conditions. The study also showed that air pollution levels in urban areas are often higher than in rural areas. This is due to various factors, such as population density, industrial activity and motor vehicle emissions. Based on the results of the study, it can be concluded that air pollution is a public health issue that needs special attention from the Government and different stakeholders. Efforts to reduce air pollution must be carried out in a sustainable manner, for example by improving the quality of public transportation, encouraging the use of renewable energy and implementing stricter pollutant emission regulations.

A thorough understanding of spatial and temporal emission and immission patterns of air pollutants in urban areas is challenged by the low number of air-quality monitoring stations available. Plants are promising low-cost biomonitoring tools. However, source identification of the trace metals incorporated in plant tissues (i.e., natural vs anthropogenic) and the identification of the best plant to use remain fundamental challenges. To this end, Nerium oleander L. collected in the city of Zaragoza (NE Spain) has been investigated as a biomonitoring tool for assessing the spatial immission patterns of airborne metals (Pb, Cu, Cr, Ni, Ce, and Zn). N. oleander leaves were sampled at 118 locations across the city, including the city center, industrial hotspots, ring-roads, and outskirts. Metal concentrations were generally higher within a 4 km radius around the city center. Calculated enrichment factors relative to upper continental crust suggest an anthropogenic origin for Cr, Cu, Ni, Pb, and Zn. Zinc isotopes showed significant variability that likely reflects different pollution sources. Plants closer to industrial hotspots showed heavier isotopic compositions (δ66ZnLyon up to +0.70‰), indicating significant contributions of fly ash particles, while those far away were isotopically light (up to -0.95‰), indicating significant contributions from exhaust emissions and flue gas. We suggest that this information is applied for improving the environmental and human risk assessment related to the exposure to air pollution in urban areas.

Large urban areas are currently facing the problem of increasingly serious air pollution, which greatly affects human health and economic development. The sources of air pollution are mainly from industrial activities, transportation and human activities. Air pollutants data can be monitored and extracted from remote sensing images. This research aims to assess the level of NO2 and SO2 pollution in the air from data collected by OMI (Ozone Monitoring Instrument) on AURA satellite of A-Train group. Pollution levels of NO2 and SO2 will be calculated, assessed. Then, a map illustrating the trend of NO2 and SO2 in Ho Chi Minh City during the period of 2015-2019 will be made. The results of the study showed that NO2 concentration varies significantly with the seasons, relatively high in the dry season reaching 70 (μg/m3) and decreasing in the rainy season. The concentration of SO2 varies relatively evenly in the area and increases in industrial areas with concentrations ranging from 0.1 to 1.37 mg/m3. Therefore, it is necessary to speed up the rapid monitoring of air pollution in urban areas and large cities in order to get accurate, fast and timely air pollution information to have more appropriate and effective mitigation and prevention measures.

Environmental management involves controlling various forms of pollution to levels that do not pose a threat to the health of the people and the environment in general. This paper presents a framework to analyze sources of local air pollution in cities. Using an OLS model, an investigation is performed of the relationship among the concentrations of air pollutants [more precisely, concentrations of sulfur dioxide (SO2), dust, nitric oxide (NO), nitrogen dioxide (NO2), carbon oxide (CO) and ozone (O3)], economic activities, and meteorology. Time series analysis leads to a model, that explains a high degree of the variance in the air pollution data. The model is applied to daily time series from three measurement stations in innsbruck, Austria. Estimation results of the model generally fit with the expected relations. Space heating influences SO2, dust, and NO, while NO2 levels are primarily affected by traffic. These results also indicate interdependent relations among the pollutants NO, NO2, O3, and CO; O3 levels depend on temperature and sunshine.

Urban residential energy switching in China between 1980 and 2014 prevents 2.2 million premature deaths

Improving regulations on residential emissions and non-criteria hazardous contaminants—Insights from a field campaign on ambient PM and PAHs in North China Plain

A spatial land use clustering framework for investigating the role of land use in mediating the effect of meteorology on urban air quality

Air pollution in urban areas is posing a great threat to urbanites all over the world. Various factors which contribute to air pollution are industrial, commercial and domestic activities. Statistics show that among these sources, motorised transportation is the highest contributing air pollution in recent past. Further, it may increase unless any major mitigation measures are planned and implemented. Therefore, assessing quality of the atmospheric air for breathing time to time is very important to control further damage. Currently, to access air quality-related data is limited. To overcome this, authors have designed and developed an air quality monitoring instrument (AQMI) using solid-state gas sensors and GPS module. It was used to measure the air pollution levels at Chennai city in real time and analysed the air pollutants levels in two different periods and suggested mitigation measures. Air quality on four routes in Chennai has been measured in 2013 and 2017 and compared in order to study the impact of air quality due to Vardhah cyclone. Four routes are considered in Chennai, namely Avadi to Tambaram (route 1—R1), Neelankari to T. Nagar (route 2—R2), Avadi to Chennai central (route 3—R3) and Tonakela camp to Redhills bus stand (route 4—R4). It is observed that the air pollution monitored in 2013 on the three routes shows about 60–70% locations having concentrations exceeding Central Pollution Control Board (CPCB) norms and one route has exceeding values at three places. After monitoring air quality in 2017, it was found that at some places, the pollution levels are increased when compared to that of 2013 due to further increase in vehicular traffic as well as Vardhah cyclone. Air quality data obtained using AQMI could serve various applications. Health conscious people could also take advantage of this to navigate through pollution-free areas. Patients with air pollution-related health problems would find data valuable to determine the less polluted routes.

In this article we report inhalation effects of nitrogen dioxide (NO2) and ozone (O3) mixture as well as O3 alone on blood-borne cancer cell colonization of lungs. The findings are discussed in light of our earlier studies with NO2 exposure alone. In all of these studies the mouse B16 melanoma model was used. Animals were exposed to ambient concentrations of pollutants before melanoma-cell infusion. The results have indicated that inhalation of NO2 played a significant role in facilitation of blood-borne cancer cell spread, while O3 inhalation did not. With respect to mechanisms involved, the role of natural immunity was investigated and its was postulated that nitrogen dioxide may affect cells of the immune system and may in part account for the results. These findings may have direct bearing on dissemination of human cancer cells, since many cancer patients have circulating cancer cells and are exposed daily to noxious air pollutants. Most importantly, this effect may be preventable by reducing air pollution in urban areas.

Vertical profiles of aerosol and NO2 based on mobile multi-axis differential absorption spectroscopy

Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behaviour has made artificial intelligence and statistical learning models as a useful tool for a more accurate pollutant concentration prediction. The aim of this study is to build a regression model of daily air quality prediction by using support vector machine (SVM) technique in Gijón urban area (Northern Spain) at local scale. To accomplish the objective, the observed data of nitrogen oxides, carbon monoxide, sulphur dioxide, ozone and dust for years 2006 to 2008 are used to create a highly nonlinear model of the air quality in the city based on SVM techniques. One objective of this model was to make a preliminary estimate of the dependence between primary and secondary pollutants in Gijón urban area at local scale. A second aim was to determine the factors with the greatest bearing on air quality in order to propose health and lifestyle improvements. This support vector regression model captures the main insight of statistical learning theory to obtain a good prediction of the dependence among the main pollutants in Gijón urban area since it shows a good agreement between the observed and predicted values of pollutants using statistical estimators as correlation coefficients, mean errors and root mean squared errors. Finally, conclusions of this study are exposed.