Concentrations Of Gaseous Pollutants Research Articles

A Preliminary Case Study on the Compounding Effects of Local Emissions and Upstream Wildfires on Urban Air Pollution

Interactions between urban and wildfire pollution emissions are active areas of research, with numerous aircraft field campaigns and satellite analyses of wildfire pollution being conducted in recent years. Several studies have found that elevated ozone and particulate pollution levels are both generally associated with wildfire smoke in urban areas. We measured pollutant concentrations at two Utah Division of Air Quality regulatory air quality observation sites and a local hot spot (a COVID-19 testing site) within a 48 h period of increasing wildfire smoke impacts that occurred in Salt Lake City, UT (USA) between 20 and 22 August 2020. The wildfire plume, which passed through the study area during an elevated ozone period during the summer, resulted in increased criteria pollutant and greenhouse gas concentrations. Methane (CH4) and fine particulate matter (PM2.5) increased at comparable rates, and increased NOx led to more ozone. The nitrogen oxide/ozone (NOx/O3) cycle was clearly demonstrated throughout the study period, with NOx titration reducing nighttime ozone. These findings help to illustrate how the compounding effects of urban emissions and exceptional pollution events, such as wildfires, may pose substantial health risks. This preliminary case study supports conducting an expanded, longer-term study on the interactions of variable intensity wildfire smoke plumes on urban air pollution exposure, in addition to the subsequent need to inform health and risk policy in these complex systems.

Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors

Low-cost gas sensors (LCS) often produce inaccurate measurements due to varying environmental conditions that are not consistent with laboratory settings, leading to inadequate productivity levels compared to high-quality sensors. To address this issue, we propose the use of Machine Learning (ML) to predict accurate concentrations of pollutant gases acquired by LCS integrated into an embedded Internet of Things platform. However, a key challenge is to optimize an accurate ML design under low memory and computation power constraints of microcontrollers (MCUs) while maintaining accurate ML scores. After data analysis and pre-processing, we assess and analyze the performance of five ML algorithms to predict the concentration of pollutants gases from multiple specifications (weather, presence of other gases, etc.). To support the experiments, datasets from three sources are used: (1) VOCSens, (2) Belgian Interregional Environment Agency cell, and (3) Visual-Crossing. Once the best model was optimized and validated, multiple hard constraints were added to the selected ML structure to satisfy material and expert requirements. Trained models were ported to be implemented locally in a MCU after comparing several porting libraries. The assembled code obtained is evaluated based on two metrics: storage memory consumption and inference time, relative to the highest attainable capacities. The improved random forest is the best ML model for the used dataset with an R2 score meeting of 0.72 and Root Means Square Error of 0.0028 ppm. The best generated Tiny-ML model needs 3% of RAM and 98% of Flash storage. The empirical results prove that the developed ML algorithm applied to LCS provides high accuracy to predict pollutant gases. This algorithm can also be used to adjust the LCS systems to provide calibrated data in real time, even if the platform being used is not particularly advanced or powerful.

Use of measurement results from a mobile laboratory in the diagnostics of anthropogenic environment - linear ordering of objects using the TOPSIS method

The measurements of the concentrations of gaseous and dust pollutants in the anthropogenic environment are an important element of environmental monitoring and for determining directions of preventive activities in the field of health protection. The article presents the results involving the concentrations of suspended dust and gaseous pollutants in the outdoor air, which were recorded at three measuring stations of air quality in the Silesian and Opole voivodeships (Wodzisław Śląski, Zabrze, Kędzierzyn-Koźle). The results were supplemented with the values recorded by the mobile laboratory located at the Center for Continuing Education - Branch of the Silesian University of Technology in Rybnik. The research results were used for a synthetic assessment of the threat level to the anthropogenic environment. In the computational layer, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was employed, which is included in the group of methods for solving multi-criteria decision-making problems (Multi Attribute Decision Making).

Cepair: an AI-powered and fog-based predictive CEP system for air quality monitoring

AbstractAir pollution is one of the influential problems threatening the environment and human health today. Therefore, it is critical to develop predictive systems for proactive decisions in solving this problem. Since the prediction of air pollution depends on several complicated factors such as the accuracy of meteorology reports, air pollution accumulation, traffic flow, and industrial emissions, the contribution of historical or real-time predictions to the solution of the problem is limited. To address the existing limitations, we propose a novel AI-powered and Fog-based predictive complex event processing system (CepAIr) for the prediction of future air pollution rates. CepAIr predicts the future air quality of pollutant gases using RNN, LSTM, CNN, and SVR models. Then, it sends the prediction results to decision-makers in an understandable format, enabling them to take proactive actions. Finally, we evaluate the performance of the CepAIr with SVR and DL models. Additionally, we examine CepAIr in terms of end-to-end network delay and measure its impact on the network. The extensive simulation results demonstrate that the CepAIr predicts future pollutant gas concentrations with DL models (especially with CNN) with a high success rate while guaranteeing minimum end-to-end network delay.

Measuring the concentrations of some gaseous air pollutants and dust particles in the western city of Hamzah in Iraq

This research concentrates on studying and analyzing the spatial and temporal variation of the concentrations of air pollutants in the Hamzah-western city during the four seasons of the year 2022 by studying the concentrations of gaseous pollutants in the air, such as NO2, SO2, CO, CO2, and O3, as well as measuring the concentrations of dust particles suspended in the air, such as PM2.5 and PM10. The number of sampling sites reached six, distributed into five different sectors (industrial, residential, transport, agricultural, and a mixed sector combining industry and transportation) in order to show the discrepancies in the concentrations of pollutants and explain their causes. The results of in situ measurements and laboratory analyses showed that there is a spatial and temporal variation in the concentrations of air pollutants. The highest concentrations of carbon monoxide CO, carbon dioxide CO2, and sulfur dioxide SO2 were recorded in sector 1 and during the summer season at concentrations of 38.5, 337, and 2.9 ppm, respectively. While the highest concentrations of nitrogen dioxide (NO2) were recorded in sector (2) with a concentration of 0.65 ppm, and all those concentrations were outside the permissible limits, the lowest concentrations were recorded for gases in sector (6), which covered all the study semesters. As for the concentrations of suspended dust particles in their sizes (PM2.5 and PM10), the highest concentrations were recorded in Sector (2), during all seasons of the study, and at 383.81 and 389.38 ppm concentrations, respectively. While the lowest concentrations of suspended particles of both sizes were recorded in Sector 6, a value of 119.65 ppm was recorded for PM2.5 minutes and 115.65 ppm for PM10 minutes. Thus, all their values during the study period were greater than the permissible limits.

Spatial Distributions of Gaseous Air Pollutants Including Particulate Matter in the Narsingdi City of Dhaka Division

ABSTRACT This study summarizes the concentration of major gaseous air pollutants in Narsingdi city of Dhaka division. To accomplish this study, we investigate fifteen sampling stations (Velanagar, Railway Station, Boro Bazar, Ghoradia, Brahmondi, Shaheprotap, Launch Terminal, Satirpara, Bus Terminal, Silmandi, Gangpar Bridge, Panchdona, Shekherchar and Anandi) of the study area. In this study we measured six gaseous air pollutantstogether with PM2.5 and PM10 during the month September, 2022 using air quality meter Aeroqual (Series500). Highest concentration of PM2.5 was detected 58 µg/m3 at Boro Bazar whereas highest concentration ofPM10 was 165 µg/m3 was at Anandi of the study area where emission from vehile, construction activities and waste burning are predominant. Moreover, in this study we found statistically significant correlation with CH4 and CO2 (r = 0.679, p > 0.01), PM2.5 and PM10 (r = 0.630, p > 0.05) indicating their sources of emission might be similar including fossil fuel burning in vehicles, industrial emissions and road dust. Furthermore, we calculated AQI value based on PM2.5 concentration and highest AQI (152) value was observed in Boro Bazarof the study area followed by Bus Terminal (129), Gangpar Bridge (117), Anandi (112), Ghoradia (102), Brahmondi (89), Panchdona (84), Satirpara (83), Shaherpotap (82), Silmandi (80), Railway Station (78), Madhabdi (78), Shekherchar (76), Launch Terminal (76), Velanagar (59) and were very much compatiblewith US consulate, Bangladesh published data.

Statistical Investigations on Relationship of Intraocular Pressure with Environmental Factors

Clinical relevance: Intraocular pressure (IOP) has a significant role in glaucoma pathophysiology. There are factors that influence in the IOP value, such as central corneal thickness or the biomechanical properties of the cornea. However, other less studied factors may influence the IOP, such as environmental pollution.
 Background: The increase in air pollution is related to acute respiratory pathologies. Regarding the eyes, is related to conjunctivitis or dry eye disease. The purpose was to analyse the correlation of environmental factors (atmospheric pressure, temperature, O3, NO2, PM10 and PM2.5 concentrations) with intraocular pressure in young healthy patients.
 Methods: A cross-sectional study was carried out on patients treated at General Optica centres in Spain and Portugal in collaboration with the University of Valladolid. This study included healthy patients (between 18 and 40 years old). IOP measurements were taken with different air tonometers (CT-80, CT-800, NCT-200, NT-510 and NT-530) for one week and correlated with environmental factors (atmospheric pressure, temperature, O3, NO2, PM10 and PM2.5 concentrations). Correlation (Spearman’s Rho) analyses were performed between IOP and the different environmental parameters. Moreover, groupings were performed as a function of the values for healthy exposure levels recommended by the WHO. Different comparisons were performed using the Mann‒Whitney U test.
 Results: Statistically significant correlations were found (p < .04) between IOP and temperature (r = 0.37), atmospheric pressure (r = 0.20), NO2 (r = 0.14), PM10 (r = 0.21), O3 (r = 0.16) and PM2.5 concentrations (r = 0.16). Regarding the IOP values of people who were exposed to unhealthy concentrations (AQI > 20) versus those who were not, higher IOP values were only found in people exposed to PM2.5.
 Conclusion: Slight correlations were found between higher temperature, atmospheric pressure and concentrations of pollutant gases and increased IOP. More clinical studies are needed to understand the role that these environmental factors play in the aqueous humour flow and the value of IOP to know if and to what extent these factors may be risk of glaucoma.

Assessment of the Ambient Air Quality of Sangli City, Maharashtra, vis-à-vis National Ambient Air Quality Standards (NAAQS)

Assessment of ambient air quality is important to formulate short-term and long-term plans to mitigate air pollution. This study attempts to present an ambient air quality assessment for Sangli City, located at 16.85° N, and 74.58° E. The assessment is done through ambient air quality data measured over three ambient air quality monitoring stations located in residential, commercial, and industrial zones of Sangli City. The data was collected from 2009 to 2017 under the National Air Quality Monitoring Program of the Central Pollution Control Board (CPCB), India. The observed concentrations of gaseous pollutants, SO2 and NOx, and respirable particulate matter are compared with the concentration levels prescribed by the National Ambient Air Quality Standards (NAAQS) in 2009. The concentration levels of particulate matter are quite on the higher side of the prescribed standards. The levels of NOx are almost approaching the prescribed values, whereas SO2 levels are well within the margin. We have also studied seasonal variations in the pollutants during the study period. In general, the air quality in Sangli City is quite undesirable during the mid-winter to mid-summer period. The study is useful for common men to increase awareness. It also forms baseline data and is useful for local and central government to design appropriate strategies to apply corrective measures, protect health, and promote environmental protection.

Overview of the Alaskan Layered Pollution and Chemical Analysis (ALPACA) Field Experiment.

The Alaskan Layered Pollution And Chemical Analysis (ALPACA) field experiment was a collaborative study designed to improve understanding of pollution sources and chemical processes during winter (cold climate and low-photochemical activity), to investigate indoor pollution, and to study dispersion of pollution as affected by frequent temperature inversions. A number of the research goals were motivated by questions raised by residents of Fairbanks, Alaska, where the study was held. This paper describes the measurement strategies and the conditions encountered during the January and February 2022 field experiment, and reports early examples of how the measurements addressed research goals, particularly those of interest to the residents. Outdoor air measurements showed high concentrations of particulate matter and pollutant gases including volatile organic carbon species. During pollution events, low winds and extremely stable atmospheric conditions trapped pollution below 73 m, an extremely shallow vertical scale. Tethered-balloon-based measurements intercepted plumes aloft, which were associated with power plant point sources through transport modeling. Because cold climate residents spend much of their time indoors, the study included an indoor air quality component, where measurements were made inside and outside a house to study infiltration and indoor sources. In the absence of indoor activities such as cooking and/or heating with a pellet stove, indoor particulate matter concentrations were lower than outdoors; however, cooking and pellet stove burns often caused higher indoor particulate matter concentrations than outdoors. The mass-normalized particulate matter oxidative potential, a health-relevant property measured here by the reactivity with dithiothreiol, of indoor particles varied by source, with cooking particles having less oxidative potential per mass than pellet stove particles.

Efficient detection of 45 ppb ammonia at room temperature using Ni-doped CeO2 octahedral nanostructures

To meet the requirements in air quality monitors for the public and industrial safety, sensors are required that can selectively detect the concentration of gaseous pollutants down to the parts per million (ppm) and ppb (parts per billion) levels. Herein, we report a remarkable NH3 sensor using Ni-doped CeO2 octahedral nanostructure which efficiently detects NH3 as low as 45 ppb at room temperature. The Ni-doped CeO2 sensor exhibits the maximum response of 42 towards 225 ppm NH3, which is ten-fold higher than pure CeO2. The improved sensing performance is caused by the enhancement of oxygen vacancy, bandgap narrowing, and redox property of CeO2 caused by Ni doping. Density functional theory confirms that O vacancy with Ni at Ce site (VONiCe) augments the sensing capabilities. The Bader charge analysis predicts the amount of charge transfer (0.04 e) between the Ni-CeO2 surface and the NH3 molecule. As well, the high negative adsorption energy (≈750 meV) and lowest distance (1.40 Å) of the NH3 molecule from the sensor surface lowers the detection limit. The present work enlightens the fabrication of sensing elements through defect engineering for ultra-trace detection of NH3 to be useful further in the field of sensor applications.

A Neural Network-Based Approach for Real-Time Measurement of the Concentration of Gaseous Pollutants in Tehran Using MODIS

A Neural Network-Based Approach for Real-Time Measurement of the Concentration of Gaseous Pollutants in Tehran Using MODIS

Development of an integrated model framework for multi-air-pollutant exposure assessments in high-density cities

Abstract. Exposure models for some criteria of air pollutants have been intensively developed in past research; multi-air-pollutant exposure models, especially for particulate chemical species, have been however overlooked in Asia. Lack of an integrated model framework to calculate multi-air-pollutant exposure has hindered the combined exposure assessment and the corresponding health assessment. This work applied the land-use regression (LUR) approach to develop an integrated model framework to estimate 2017 annual-average exposure of multiple air pollutants in a typical high-rise and high-density Asian city (Hong Kong, China) including four criteria of gaseous air pollutants (particulate matter with an aerodynamic diameter equal to or less than 10 µm (PM10) and 2.5 µm (PM2.5), nitrogen dioxide (NO2), and ozone (O3)), as well as four major PM10 chemical species. Our integrated multi-air-pollutant exposure model framework is capable of explaining 91 %–97 % of the variability of measured gaseous air pollutant concentration, with the leave-one-out cross-validation R2 values ranging from 0.73 to 0.93. Using the model framework, the spatial distribution of the concentration of various air pollutants at a spatial resolution of 500 m was generated. The LUR model-derived spatial distribution maps revealed weak-to-moderate spatial correlations between the PM10 chemical species and the criteria of air pollutants, which may help to distinguish their independent chronic health effects. In addition, further improvements in the development of air pollution exposure models are discussed. This study proposed an integrated model framework for estimating multi-air-pollutant exposure in high-density and high-rise urban areas, serving an important tool for multi-air-pollutant exposure assessment in epidemiological studies.

The effect of human activities on Bali’s Silent Day (Nyepi) in 2022

During Nyepi, all activities are eliminated, including public services, such as closing access to land, sea, and air transportation routes that transit to Bali for one day. In 2022, the BMKG Research and Development Center observed air quality in Nyepi. The measurement aims to determine the relative reduction level of greenhouse gas (GHG) and particulate emissions on Nyepi Day compared to before and after. Greenhouse gases and particulates were measured for seven days, February 28 - March 6, 2022. The measurement locations were over 3 points: Denpasar Regional Office III, Karangasem Geophysics Post, and Jembrana Climatology Station. The results of this field measurement show that daily human activities significantly influence the concentration of pollutant gases and particulates in the air. During Nyepi Day, air concentration improves when all human actions are reduced. This is marked by a decrease in the air’s concentration of pollutant gases (CO, NO2) and dust particles. The reduction in the concentration of pollutant gases and dust particles did vary at each observation location; this was due to the sensitivity of the equipment, the character of each observation location, and the placement of measuring devices. Urban areas filled with community activities have experienced the greatest improvement in air quality compared to suburban areas. Order of measuring instruments close to the location of pollutant sources, even in suburban areas, will decrease the concentration of pollutant gases, similar to that in urban areas. The difference between PM25 data during Nyepi 2022 and the average PM25 for 2020 - 2022 shows a decrease in PM25 concentrations of around 47%, with the highest drop at night.

Higher temperature and humidity exacerbate pollutant-associated lung dysfunction in the elderly

RationaleAir pollution and extreme temperature and humidity are risk factors for lung dysfunction, but their interactions are not clearly understood. ObjectivesTo assess the impact of exposure to air pollutants and meteorological factors on lung function, and the contribution of their interaction to the overall effect. MethodsThe peak expiratory flow rates of 135 participants were repeatedly measured during up to four visits. Two weeks before each visit, the concentrations of gaseous pollutants and 19 fine particle components, and the temperature and relative humidity, were continuously monitored in the community where they lived. A Bayesian Kernel machine regression model was used to explore the non-linear exposure-response relationships of the peak expiratory flow rate with pollutant exposure and meteorological factors, and their interactions. Measurements and main resultsIncreased temperature and relative humidity could exacerbate pollutant-associated decline in the peak expiratory flow rate, although their associations with lung dysfunction disappeared after adjustment for pollutant exposure. For example, declines of peak expiratory flow rate associated with interquartile range increase of 3-day cadmium exposure were −0.03 and −0.07 units, when temperature was at 0.1 and 19.5 °C, respectively. Decreased temperature were associated with declines of peak expiratory flow rate after adjustment for pollutant exposure, and had interaction with pollutant exposure on lung dysfunction. ConclusionsHigh temperature, low temperature, and high humidity were all high-risk factors for lung dysfunction, and their interactions with pollutant levels contributed greatly to the overall effects.

Acute exposure to realistic simulated urban atmospheres exacerbates pulmonary phenotype in cystic fibrosis-like mice

Cystic Fibrosis (CF) is a lethal genetic disorder caused by pathogenic mutations of the CFTR gene. CF patients show a high phenotypic variability of unknown origin. In this context, the present study was therefore dedicated to investigating the effects of acute exposure to air pollution on the pulmonary morbidity of a CF-like mice model. To achieve our aim, we developed a multidisciplinary approach and designed an innovative protocol using a simulation chamber reproducing multiphasic chemical processes at the laboratory. A particular attention was paid to modulate the composition of these simulated atmospheres, in terms of concentrations of gaseous and particulate pollutants. Exposure to simulated urban atmospheres induced mucus secretion and increased inflammatory biomarkers levels, oxidative stress as well as expression of lung remodeling actors in both WT and CF-like mice. The latter were more susceptible to develop such a response. Though we could not establish direct mechanistic link between biological responses and specific components, the type of immune response induced depended on the chemical composition of the atmospheres. Overall, we demonstrated that air pollution is an important determinant of CF-like lung phenotypic variability and emphasized the added value of considering air pollution with a multi-pollutant approach.

Vehicular Volume and Concentration of Gaseous Atmospheric Pollutants and Particulate Matter at Selected Road Junctions in Port Harcourt, Nigeria

The increase in industrial and vehicular activities has led to the proliferation of atmospheric pollutants in cities globally. The Niger Delta region is an industrial hub and serves as the nation's resource base by exploring crude oil. This work postulated that there would be high pollutants and particulate matter at road junctions due to the multiplicity of several vehicles owned by the surging human population. The number of vehicles at each junction were counted. A multi-parameter gas monitor (A multi–RAE PLUS (PGM-50) was used to collect air samples at different junctions (n = 5). The concentration of nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), particulate matter (PM2.5, PM10), temperature, relative humidity, and wind were analyzed. The result revealed that the volume of the vehicles at road traffic junctions follows the order Rumuola>Mile 3/UST Roundabout>Choba Junction>Garrison>Lagos Bus Stop. Choba Junction had the highest concentration of pollutants. The highest concentration of vehicular pollutants was observed in the morning. Carbon monoxide (CO) had the highest concentration, followed by SO2 and NO2. While for particulate matter, PM2.5 had a higher concentration than PM10. The result implies that the number of vehicular traffic does not necessarily determine the concentration of pollutants because other anthropogenic activities are causing a spike in atmospheric pollutants within the city.

A quantitative assessment of natural and anthropogenic effects on the occurrence of high air pollution loading in Dhaka and neighboring cities and health consequences

Although existing studies mainly focused on the air quality status in Bangladesh, quantifying the natural and manmade effects, the frequency of high pollution levels, and the associated health risks remained beyond detailed investigation. Air quality and meteorological data from the Department of Environment for 2012–2019 were analyzed, attempting to answer those questions. Cluster analysis of PM2.5, PM10, and gaseous pollutants implied that Dhaka and neighboring cities, Narayangonj and Gazipur, are from similar sources compared to the other major cities in the country. Apart from the transboundary sources, land use types and climate parameters unevenly affected local pollution loadings across city domains. The particulate concentrations persistently remained above the national standard for almost half the year, with the peaks during the dry months. Even though nitrogen oxides remained high in all three cities, other gaseous pollutants, such as CO and O3, except SO2, showed elevated concentrations solely in Dhaka city. Concentrations of gaseous pollutants in Dhaka vary spatially, but no statistical differences could be discerned between the working days and holidays. Frequency analysis results and hazard quotients revealed the likelihood of adverse health outcomes in Narayangonj ensuing from particulate exposures surpasses the other cities for different age, gender, and occupation groups. Nonetheless, school-aged children and construction workers were most at risk from chronic exposure to gaseous pollutants mostly in Dhaka. One limitation of this study was that the routine air quality monitoring happens just from five sites, making the evidence-based study concerning health outcomes quite challenging.

Determination of the Spatial Distribution of Air Pollutants in Bucheon, Republic of Korea, in Winter Using a GIS-Based Mobile Laboratory.

Driven by industrialization and urbanization, urban air pollution can increase respiratory, heart, and cerebrovascular diseases, and thus mortality rates; as such, it is necessary to improve air quality through the consideration of individual pollutants and emission sources. In Republic of Korea, national and local governments have installed urban and roadside air quality monitoring systems. However, stations are lacking outside metropolitan regions, and roadside stations are sparsely distributed, limiting comparisons of pollutant concentrations with vehicle traffic and floating population levels. Local governments have begun using mobile laboratories (MLs) to supplement the fixed measurement network and investigate road pollution source characteristics based on their spatiotemporal distribution; however, the collected data cannot be used effectively if they are not visualized. Here, we propose a method to collect and visualize global information system (GIS)-based air quality data overlayed with environmental variables to support air quality management measures. Spatiotemporal analyses of ML-derived data from Bucheon, Korea, confirmed that particulate and gaseous pollutant concentrations were high during typical commuting hours, at intersections, and at a specially managed road. During commuting hours, the maximum PM10 concentration reached 200.7 µg/m3 in the Nae-dong, Gyeongin-ro, and Ojeong-dong ready-mix concrete complex areas, and the maximum PM2.5 concentration was 161.7 µg/m3. The maximum NOx, NO2, and NO levels of 1.34 ppm, 0.18 ppm, and 1.18 ppm, respectively, were also detected during commuting hours. These findings support the need for targeted management of air pollution in this region, and highlight the benefit of comprehensively comparing road levels, driving speed, and traffic levels when identifying hotspots of air pollution. Such analyses will contribute to the development of air quality management measures customized to regional characteristics.

The dispersion of various pollutants emitted from truck and passenger vehicles: a wind-tunnel study.

In recent years, investigation of the dispersion of exhaust gas from vehicles has received increasing attention in various studies using various approaches, including on-field measurements, experimental laboratory models, and software simulations. This study aimed to investigate the effects of changes in vehicle traffic conditions, fuel type used in an engine, and vehicle size on the emission and dispersion of gaseous pollutants from the exhaust pipe in a wind tunnel. The results showed that pollutant concentration decreases along the tailpipe centerline as the exhaust gas moves backward and the flow pattern changes significantly from 11.5 to 15 m/s. However, for the passenger car, the small recirculation region in the near-wake region at an inflow air velocity of 11.5 m/s carries away more particles compared to an incoming velocity of 15 m/s, resulting in lower gaseous concentrations in both longitudinal and transverse directions. The gaseous dispersion behind the truck and the car is wider for an inflow air velocity of 11 m/s, especially for NO, NO2, and SO2. The results also showed that the pollutant concentrations of the truck exhaust plume were higher than those of the passenger car in lower inflow air velocity in both longitudinal and transverse directions. The lower concentration of dispersed gaseous pollutants behind the passenger car model compared to the truck model may be attributed to the shape and height of the truck model, which can trap more particles behind the vehicle. Finally, the dispersion pattern of SO2 is almost identical to that of NO2, and the dispersion of the exhaust gas plume related to CO and NO pollutants is more prolonged than that of CO2 and NO2 particles along the tailpipe centerline. The findings of this study may help in estimating the concentration of pollutants along urban highways.

Assessment/monitoring of air pollution and effect on human health in Kota city during Spring season - 2020

National Clean Air Program (NCAP), a national initiative started in 2019, in response to the ongoing failure of Indian cities to achieve air quality norms. One of its key goals is to reduce the concentration of Particulate Matter (PM) by 20 % to 30 % compared to 2017 levels by the year 2024. The main objective of this work is to analyze the percentage of the air pollutants (respirable suspended particulate matter) in the month of January 2020 till March 2020 at Kota City in Rajasthan. The observed data collected during this time was analyzed and mitigation measures were suggested based on the NCAP guidelines. Finally, the measured metrics are also compared with the ambient air quality (AAQ) standards of the Central Pollution Control Board (CPCB). The current data indicates that the concentrations of gaseous pollutants like SO2 and NO2 remain below the AQI permissible limit, which is 80 µg/m3 while the level of PM10 is determined to be higher than the allowed limit of 100 µg/m3. This is especially in the colder season when temperature inversions are more likely to happen. It occurs when a layer of warm air traps cooler air near the surface. This can prevent the dispersion of pollutants, including PM10, leading to their accumulation in the lower atmosphere.