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Declining air quality is highly ignored and very common form of degradation of the environment in nations that are both developed and developing. There are several contaminants in the air that have been identified in various studies on air pollution. The crucial parameters of all air pollutants are gaseous and particle pollution. The present study was undertaken to estimate the quality of ambient air in Chandrapur district, Maharashtra state of India. In this paper, an effort has been made to study the standing and trend of Sulphur dioxide (SO2), Oxides of Nitrogen (NOx), Carbon Monoxide (CO), Ozone (O3), Ammonia (NH3), Respirable Suspended Particulate Matter i.e.; PM10, PM2.5, toxic pollutants i.e.; lead, arsenic, nickel, benzo [a] pyarene and benzene and hydrocarbons. The results clearly show that, all parameters of ambient air quality monitoring values were some shown slightly below permissible limit. The ambient air quality monitoring data show that the Chandrapur district has substantial air pollution concerns in terms of SO2, NOx, PM, and other air pollutants. However, after comparing these values with NAAQS levels, the yearly averages of these air pollutants have been found to be below than the NAAQS levels. However, without effective mitigation measures, the concentrations of these pollutants will rise at quite alarming rate. [ABSTRACT FROM AUTHOR]

Over the past decades, air pollution has turned out to be a major cause of environmental degradation and health effects, particularly in developing countries like India. Various measures are taken by scholars and governments to control or mitigate air pollution. The air quality prediction model triggers an alarm when the quality of air changes to hazardous or when the pollutant concentration surpasses the defined limit. Accurate air quality assessment becomes an indispensable step in many urban and industrial areas to monitor and preserve the quality of air. To accomplish this goal, this paper proposes a novel Attention Convolutional Bidirectional Gated Recurrent Unit based Dynamic Arithmetic Optimization (ACBiGRU-DAO) approach. The Attention Convolutional Bidirectional Gated Recurrent Unit (ACBiGRU) model is determined in which the fine-tuning parameters are used to enhance the proposed method by Dynamic Arithmetic Optimization (DAO) algorithm. The air quality data of India was acquired from the Kaggle website. From the dataset, the most-influencing features such as Air Quality Index (AQI), particulate matter namely PM2.5 and PM10, carbon monoxide (CO) concentration, nitrogen dioxide (NO2) concentration, sulfur dioxide (SO2) concentration, and ozone (O3) concentration are taken as input data. Initially, they are preprocessed through two different pipelines namely imputation of missing values and data transformation. Finally, the proposed ACBiGRU-DAO approach predicts air quality and classifies based on their severities into six AQI stages. The efficiency of the proposed ACBiGRU-DAO approach is examined using diverse evaluation indicators namely Accuracy, Maximum Prediction Error (MPE), Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Correlation Coefficient (CC). The simulation result inherits that the proposed ACBiGRU-DAO approach achieves a greater percentage of accuracy of about 95.34% than other compared methods. [ABSTRACT FROM AUTHOR]

Hair salons use many hair products that have toxic chemicals in them. These toxic chemicals include volatile organic compounds, formaldehyde, and particulate matter. Daily exposure to these pollutants causes severe health issues in the long run. This study aims to find the concentration of the air pollutants such as PM1, PM2.5, PM10, TVOC, CO2, and formaldehyde in four hair salons located in Coimbatore, Tamil Nadu, India. In this paper, we propose an IoT-based air quality monitoring system with integrated sensors to monitor the concentration of air pollutants remotely via ThingSpeak data analytics cloud platform in hair salons. The maximum 15 min average concentration values of PM1, PM2.5, and PM10 were 128, 154, and 169 µg/m3 respectively. The TVOC levels exhibited a rapid increase of about 80–90% during facials and hair gel application and a peak value of about 5248.25 ppb was measured at salon 2. Also, weekend and weekday comparison is done. It was found that the weekend concentrations of the measured pollutants are comparatively higher than weekday concentrations. After analyzing the pollutant concentration, the effects of primary health parameters such as blood pressure and pulse rate of the hairdressers are measured. One-third of the hairdressers displayed high blood pressure values with a maximum of 161/104 which falls under stage 2 hypertension. Also, secondary parameters such as temperature, humidity, ventilation type, and number of customers are also measured. From the overall analysis, it is suggested that adequate ventilation and regulated product usage are said to reduce the effects of indoor air pollution. [ABSTRACT FROM AUTHOR]

In order to survive, mankind needs air. The quality of life depends on the purity of the air we breathe in. Hazardous pollutants are stirred up in our environment by various activities everyday. In a developing country like India, which has huge population, protecting public health is important. In general, air quality is measured using the air quality index, which records the pollutants level in the air. These recordings are to be mentioned across various places, to know about the air quality level. The advancement of the recent artificial intelligence can be replaced by the human efforts and to automate the system in flowless manner. Hence, an effort has been taken in this paper, by proposing a framework of predicting the air quality seasonally using regularized combinational LSTM (REG-CLSTM). For an efficient air quality level prediction with an improved error rate, time, and accuracy, the study has implemented Reg-CLSTM with a large amount of real-time dataset. To improvise the comprehensiveness and the potentiality of the proposed model, the significant feature is extracted by the rough set-wrapper method. The significant challenge is to provide a seasonal limit range for each considered pollutant in place of a generalized range partition. Aiming at this problem, the proposed model is able to identify the highest concerning pollutants occurrence in individual seasons. Through this study, pyramid learning-based hybridized deep learning framework is developed which can play a crucial role in warning the policy maker to reduce the activities that instigate air pollution. [ABSTRACT FROM AUTHOR]

In this paper, we have evaluated the bioaccumulation of metals by Atrichum angustatum, which is a readily available moss species in the Brahmaputra valley, India. A systematic investigation of metallic pollutants in the atmosphere was carried out using A. angustatum as a biomonitor collected from representative locations during three seasons viz. winter, pre-monsoon, and monsoon (n = 99) during the year 2018. The study was done in four unique habitations of the Brahmaputra Valley, which were further divided into three landuse areas: residential, roadside, and industrial. The highest accumulations were seen against Ca, Mg, Zn, and Fe. The calculated contaminant factors and ecological risk indices suggest that the Brahmaputra Valley is mostly contaminated by Cr, Zn, Cu, Ni, and Pb, and these metals pose a maximum ecological risk. The accumulation trend of metallic pollutants was site-specific, but most metals showed positive seasonal accumulation. A significant difference in spatial and seasonal accumulation patterns was specific to metal species. Principal component analysis (PCA) and inter-species correlations revealed that the air quality of Brahmaputra valley was greatly affected by coal burning, vehicular emission, biomass burning, road dust, and crustal dust. Finally, the study led us to the conclusion that A. angustatum can serve as a potential biomonitor for metallic pollutants. [ABSTRACT FROM AUTHOR]

In the past few decades, many urban areas around the world have suffered from severe air pollution and the health hazards that come with it, making gathering real-time air quality and air quality forecasting very important to take preventive and corrective measures. This paper proposes a scalable architecture to monitor and gather real-time air pollutant concentration data from various places and to use this data to forecast future air pollutant concentrations. Two sources are used to collect air quality data. The first being a wireless sensor network that gathers and sends pollutant concentrations to a server, with its sensor nodes deployed in various locations in Bengaluru city in South India. The second source is the real-time air quality data gathered and made available by the Government of India as a part of its Open Data initiative. Both sources provide average concentrations of various air pollutants on an hourly basis. Due to its proven track record of success with time-series data, a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model was chosen to perform the task of air quality forecasting. This paper critically analyses the performance of the model in two regions that exhibit a significant difference in temporal variations in air quality. As these variations increase, the model suffers performance degradation necessitating adaptive modelling. [ABSTRACT FROM AUTHOR]

Air pollution refers to the release of pollutants into the air that jeopardises human health and also results in serious environmental problems. Many countries are suffering from heavy air pollution. The inhalation of pollutant will lead to acute conditions like asthma, chronic lung diseases, and cancer. The Air Pollution not only harms the health of both humans and animals but also abolish the life of the plants. Hence the estimation of Air Quality is an essential. This paper employs air quality estimation using Particle Swarm Optimization. Multilayer perceptron type of back-propagation Neural Network is used to analyse the status of air pollution at various locations in India. The efficiency of the proposed model is evaluated with K-Nearest Neighbor, Random Forest and Support Vector Machine algorithms by classifying the data into six classes of pollution levels, which can be simply understood by the public as Good, Satisfactory, Moderately Polluted, Poor, Very Poor and Severe. [ABSTRACT FROM AUTHOR]

The chaotic structure of air pollution, human health, the evidence of cointegration and causality between the air pollution, transportation, tourism, construction, and manufacturing industries and the evidence of cointegration and causality between the air pollution, transportation, tourism, construction, and manufacturing industries and mortality rate were explored for China, India, and Turkey in the period of 1975-2018 by using four different methods. Firstly, Lyapunov and Shannon tests were applied to determine chaotic dynamics. The maximum Lyapunov test, for the selected variables, found the evidence of chaotic dynamics. Secondly, Fourier ADF and NL tests were applied. Fourier unit root test determined stationary of the variables. Thirdly, bootstrapping autoregressive distributed lag with Fourier transformation (FBARDL) was used to determine the evidence of cointegration between the variables with two different models. FBARDL test determined that the manufacturing, tourism, transport, and construction industries, air pollution, and mortality rate have evidence of cointegration in different two models. Lastly, the causality test with Fourier transformation was used to determine the direction of causality between the variables. Granger causality test determined that there is evidence of one-way causality running from transportation, tourism, construction, and manufacturing industries to air pollution and mortality rates. Accordingly, the results of this paper suggest air pollution and human health have chaotic behaviors. Air pollution has a complex, multi-variable, and multi-coupling system. Air pollutants influencing factors and air pollution itself have adverse effects on human health. [ABSTRACT FROM AUTHOR]

Building an effective clean air action plan for a city's air pollution problem requires an extensive network of monitoring stations to represent spatial and temporal patterns and an understanding of the sources contributing to the problem. In India, the National Clean Air Programme (NCAP) was launched in 2019 to develop clean air action plans for 131 non-attainment cities, which includes conducting source apportionment studies and establishing emission baselines. As of February 2023, only 39 cities have completed the apportionment studies. In this paper, we present the essential resources needed to strengthen the ambient air monitoring networks, for designing a representative airshed size, sampling size, and sampling frequency, to effectively track the progress made in the cities for better air quality. The NCAP cities were grouped into 104 airsheds (5.3% of the national area), collectively representing a total of 164 cities and a total population of 295 million (21% of the national total). Of these airsheds, 73 contain only one city; 18 contain two cities, and nine contain three cities. Four airsheds – Delhi, Mumbai, Indore, and Chandigarh contain 10, 8, 5, and 5 cities respectively. To measure and analyse particulate matter pollution, a total of 2118 sampling sites are recommended for the 104 airsheds. Cities could consider hybrid monitoring networks by complementing existing regulatory monitoring network with a high-density network of low-cost/sensor-grade monitors. An airshed level air quality management plan, an enhanced monitoring network, and consolidation of information on emission sources, are crucial for optimizing the clean air efforts under NCAP. [Display omitted] • Defined 104 airsheds to represent 131 non-attainment cities under NCAP. • Defined the minimum number of monitors required in the airsheds. • Conducted high-resolution meteorological modelling to determine minimum filter sampling frequency per airshed. • Examples of hybrid networks to reduce monitoring costs. [ABSTRACT FROM AUTHOR]

The current study was conducted around the province of Haldwani City, Uttarakhand, India, to understand the seasonal variation of ambient air pollutants (PM2.5, PM10, SO2, and NO2) and their impact on four tree species, i.e., neem (Azadirachta indica), mountain cedar (Toona ciliate), bottlebrush (Callistemon citrinus), and guava (Psidium guajava) during 2020–2021. Multiple linear regression (MLR)-based prediction analysis showed that the selected air quality variables (PM2.5, PM10, SO2, and NO2) had a significant impact on the biochemical responses of selected tree spp. including, pH, ascorbic acid (AA), total chlorophyll content (T. Chl.), relative water content (RWC), and dust deposition potential. In this, the coefficient of variance (R2) of the developed models was in the range of 0.70–0.98. The ambient air pollutants showed significant seasonal variations as depicted by using the air pollution tolerance index (APTI) and anticipated performance index (API). The tree species from polluted sites observed more pollution tolerance than the tree species from the control site. Regression analysis showed a significant positive association between the biochemical characteristics and APTI, with the highest influence by AA (R2 = 0.961) followed by T. Chl., RWC, and pH. The APTI and API score was observed as maximum for A. indica and minimum for C. citrinus. The impact of air pollutants on the morphology of foliar surface was investigated by the scanning electron microscopy (SEM) and recorded various dust deposition patterns, stomatal blockages, and damage of guard cells in the trees growing along the polluted site (S2). The present study can assist environmental managers to examine the pollution-induced variables and develop an effective green belt for combating air pollution in polluted areas. [ABSTRACT FROM AUTHOR]

The rapid urbanization and industrialization in many parts of the world have made air pollution a global public health problem. A study conducted by the Swiss organization IQAir indicated that 22 of the top 30 most polluted cities in the world are in India. This creates the problem of air pollution, which is very relevant to India as well. Exposure to air pollutants has both acute (short-term) and chronic (long-term) impacts on health. Among the major air pollutants, particulate matter 2.5 (PM2.5) is the most harmful, and its long-term exposure can impair lung functions. Pollutant concentrations vary temporally and are dependent on the local meteorology and emissions at a given geographic location. PM2.5 forecasting models have the potential to develop strategies for evaluating and alerting the public regarding expected hazardous levels of air pollution. Accurate measurement and forecasting of pollutant concentrations are critical for assessing air quality and making informed strategic decisions. Recently, data-driven machine learning algorithms for PM2.5 forecasting have received a lot of attention. In this work, a spatiotemporal analysis of air quality was first performed for Hyderabad, indicating that average PM2.5 concentrations during the winter were 68% higher than those during the summer. Following that, PM2.5 modelling was done using three different techniques: multilinear regression, K-nearest neighbours (KNN), and histogram-based gradient boost (HGBoost). Among these, the HGBoost regression model, which used both pollution and meteorological data as inputs, outperformed the other two techniques. During testing, the model acquired an amazing R² value of 0.859, suggesting a significant connection with the actual data. Additionally, the model exhibited a minimum Mean Absolute Error (MAE) of 5.717 μg/m³ and a Root Mean Square Error (RMSE) of 7.647 μg/m³, further confirming its accuracy in predicting PM2.5 concentrations. In our investigation, we discovered that the HGBoost3 model beat other PM2.5 modelling models by having the lowest error and the highest R² value. This study made a substantial addition by incorporating the spatiotemporal relationship between air pollutants and meteorological variables in predicting air quality. This method has the potential to improve the creation of more precise air pollution forecast models. [ABSTRACT FROM AUTHOR]

The impact of air pollution on the roadside plants in the Hadapsar suburban was determined using six different plant species. To assess the effects of air pollutants on roadside plants of Hadapsar area in Pune city, APTI (Air Pollution Tolerance Index) was determined of six plant species. Plants species selected for the study were Polyalthia long folia, Ficus racemosa, Terminalia catappa, Ficus benghalensis, Ficus religiosa, Samanea saman of nearly the same age size and were found in abundance. Physiological and biochemical parameters such as pH, Relative water content (RWC), Total Chlorophyll (T.C.), Ascorbic acid (A.A.) contents were determined to calculate APTI. Results indicated that all of the plants selected for the study were found to be sensitive & heavily impacted due to air pollution. Thus, these plants can be used as Bio-indicators in monitoring air pollution. [ABSTRACT FROM AUTHOR]

Vehicular pollution is one of the major sources of air pollution in urban locales that have reportedly elevated concentrations of air pollutants. This study aims to examine the performance of two air quality dispersion models, STREET and CALINE 4 to predict pollutant concentrations for an urban monitoring location that is en route to the high traffic volumes in Shimla, Himachal Pradesh, India. This study will compare the predicted and observed concentrations (for the urban monitoring location) using both quantitative and statistical methods for the 2 years of the study. The pollutant selected for the study is PM10. It was observed from the modeling studies that the performance of CALINE4 was slightly better than the STREET model. The models selected to a certain extent are defined by the available parameters for successful run completions. The application of detailed modeling studies is the first of its kind for the study location, to the best of the authors' knowledge. Hence, the application of basic and simplistic models and the examination of their performance could potentially find the best fit model to predict approximately precise concentrations. Further scope of this study should include the use of advanced air quality dispersion models for the improved prediction of concentrations. [ABSTRACT FROM AUTHOR]

To mitigate the impacts of the pandemic of coronavirus disease 2019 (COVID-19), the Indian government implemented lockdown measures on 24 March 2020, which prohibited unnecessary anthropogenic activities, thus leading to a significant reduction in emissions. To investigate the impacts of this lockdown measure on air quality in India, we used the Community Multi-Scale Air Quality (CMAQ) model to estimate the changes of key air pollutants. From pre-lockdown to lockdown periods, improved air quality is observed in India, indicated by the lower key pollutant levels such as PM 2.5 (- 26 %), maximum daily 8 h average ozone (MDA8 O 3) (- 11 %), NO 2 (- 50 %), and SO 2 (- 14 %). In addition, changes in these pollutants show distinct spatial variations with the more important decrease in northern and western India. During the lockdown, our results illustrate that such emission reductions play a positive role in the improvement of air quality. Significant reductions of PM 2.5 concentration and its major components are predicted, especially for secondary inorganic aerosols that are up to 92 %, 57 %, and 79 % for nitrate (NO 3-), sulfate (SO 42-), and ammonium (NH 4+), respectively. On average, the MDA8 O 3 also decreases 15 % during the lockdown period although it increases slightly in some VOC-limited urban locations, which is mainly due to the more significant reduction of NO x than VOCs. More aggressive and localized emission control strategies should be implemented in India to mitigate air pollution in the future. [ABSTRACT FROM AUTHOR]

Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM 2.5 and O3) using state-of-the-science emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM 2.5 speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O3 and PM 2.5 levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions. Observed and predicted ozone levels over New Delhi during September 2015, December 2015, and September 2016 routinely exceeded the 8 h Indian standard of 100 µ g m -3 , and, on occasion, exceeded 180 µ g m -3. PM 2.5 levels are predicted over the SOMAARTH headquarters (September 2015 and September 2016), Bajada Pahari (a village in the surveillance site; September 2015, December 2015, and September 2016), and New Delhi (September 2015, December 2015, and September 2016). The predicted fractional impact of residential emissions on anthropogenic PM 2.5 levels varies from about 0.27 in SOMAARTH HQ and Bajada Pahari to about 0.10 in New Delhi. The predicted secondary organic portion of PM 2.5 produced by household emissions ranges from 16 % to 80 %. Predicted levels of secondary organic PM 2.5 during the periods studied at the four locations averaged about 30 µ g m -3 , representing approximately 30 % and 20 % of total PM 2.5 levels in the rural and urban stations, respectively. [ABSTRACT FROM AUTHOR]

Air pollution is a very serious current environmental issue of human society. Large parts of countries, especially the densely populated cities, having high vehicular movement, industries, and factories, are worst affected. Biomarkers are changes in plant parameters that help in easy assessment of the environmental quality of an area at a certain time. Plants can react to different environmental stresses with the most evident responses shown by the leaves. In the present work, we have studied changes in biochemical parameters in the leaves of a mango plant (Mangifera indica), a very common plant in West Bengal, India, which were collected from four different locations in the city of Kolkata which has a high concentration of air pollutants and one control area from a rural region having a low concentration of air pollutants. It was observed that leaves which were exposed to high amounts of harmful air pollutants showed higher accumulation of molecules such as phenol, proline, malondialdehyde, and cellulose with lower amounts of chlorophyll. From this, we can observe that common environmental stress such as air pollution leads to a change in the synthesis of bioactive molecules to resist the effect of stress on the plant. Thus, from these data, it can be concluded that biochemical parameters can serve as efficient biomarkers of the air quality of an area. [ABSTRACT FROM AUTHOR]