Forecasting of air quality in Delhi using principal component regression technique

In the last few decades, global concerns have been raised about the health impacts caused by deterioration in the air quality mainly due to large‐scale industrialization and urbanization. In order to assess the extent of impacts due to rapid industrialization and commercial activities at the Integrated Industrial Estate‐Pantnagar (IIE‐Pantnagar), the ambient air quality was continuously monitored from June 2007 to May 2008. The measured 24‐h average criteria pollutants, namely, sulfur dioxide (SO2), oxides of nitrogen (NO2), respirable suspended particulate matter (RPM) and suspended particulate matter (SPM) were used for the development of the air quality index (AQI). Among the monitored air quality variables, SPM revealed as the major pollutant followed by RPM with annual average concentrations of 281.8 and 137.6 µg/m3, respectively. However, the average concentrations of gaseous pollutants (SO2 and NO2) were 21.1 and 21.7 µg/m3 respectively, which were lower than the prescribed national standards. The computed AQI reveals a gradual increasing trend with a range of 48.02–79.25, signifying the prevalence of moderate to heavy pollution levels. A comparison of the exceedence factors (EFs) divulges that suspended and respirable particulate matters are the most significant polluting agents, causing maximum deterioration in the ambient air quality. Copyright © 2010 Curtin University of Technology and John Wiley & Sons, Ltd.

Rapid urbanization and globalization has led to severe degradation of existing air quality in the majority of Indian cities. In this context, the general public has been aware of their exposure to ambient air quality and the effects of such air pollutants on human health. Hence, the concept of Air quality indices (AQI) is often used by regulatory authorities in conveying the status of existing ambient air quality to the general public. The chapter presents the application of air quality indices for assessing the existing air quality standards in an Indian city, Dharamshala, a tourist location in Himachal Pradesh, for the period of 2016-2017. Two different methods of determining AQI have been used wherein one method is used as the revised Central Pollution Control Board (CPCB), India with different sub-indices for Indian conditions based on the United States Environmental Protection Agency (USEPA) methodology and another alternative method utilizes contribution from all criteria pollutants.

The present study deals with the assessment of ambient air quality at six different locations in Cuddalore region, Tamil Nadu, India. The 24 hour s average concentration of suspended particulate matter (SPM), nitrogen dioxide (NO 2 ) and sulphur dioxide (SO 2 ) were monitored at cuddalore region, Tamil Nadu, India. The 24 hour concentration of SPM, NO 2 and SO 2 were 132.34 - 197.45, 56.33 - 89.71 and 58.78 8 0.68 µg/m 3 respectively. In present study, it was observed that concentration of NO 2 ; SO 2 levels exceed the permissible limit of National Ambient Air Quality Standard (NAAQS) for residential area. Exposures of these pollutants (NO 2 and SO 2 ) above standard levels (NAAQS) causes serious health effects on human health, associated with cardiovascular and respiratory diseases, increased risk of premature birth morbidity and even mortality. There is a significant correlation between SPM with NO 2 (r = 0.53), and S O 2 (r = 0.46) with linear regression also analyzed. Air Quality Index (AQI) values are in order of S1 > S4 > S5 > S6 > S3> S2, and, it is clearly indicates that among six locations, Nellikuppam (S1) is highly polluted and Thiruvandipuram (S2) is moderately polluted. This is due to combustion

An Air Quality Index (AQI) is a means of evaluating air quality in respect to the relation between air quality and the entire environment of an area. The main objective of the study was to compare the development of AQI using global methods and local methods to identify which could represent the more significant pattern from a spatial perspective in Dhaka city (DNCC and DSCC). The pollutants used in this study are CO, NO2, SO2, O3, CH4, CO2, PM2.5 and PM10. For the global AQI, indexing was performed using the guidelines in ‘Technical Assistance Document for the Reporting of Daily Air Quality – the Air Quality Index’ by USEPA (United States Environmental Protection Agency). This method identified and mapped the most responsible pollutant that represents the pollution pattern of the area. For local AQI, an overlay method was developed where all the relevant pollutants were weighed according to their significance and summed; their weights calculated using a Principal Component Analysis (PCA). Component analysis could find a better-correlated variable to represent the distribution from all the variables, deeming it a good feature-selecting tool. The result from this study suggests that the local AQI better represents the air quality of the study corresponding to certain real scenarios. Mapping the pollution has helped in validating the spatial pattern of the pollutants. Such methods, which include all the variables influencing the atmospheric dynamics, could establish a better pattern for the local environment. The temporal background of the factors could be considered as situations may change over time. For air quality, this could be performed with any variables apart from atmospheric constituents and can be even replicated for any component of the environment, which may help in comprehending the local environmental quality. The study would be useful to devise a proper system to develop AQI considering all the spatial and temporal effects. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 11(1), 2022: 131-149

Background/Objectives: The prime objective of this work is to study the condition and quality of the air in Coimbatore city by measuring Air Quality Index (AQI) and to compare the measured values with standard values. This work also examines the variation of ambient air quality with various climatic conditions. Methods/Statistical Analysis: The quality of air was determined based on National Ambient Air Quality Standards (NAAQS). This was carried out based on measuring four major air pollutants such as PM10, PM2.5, SO2 and NO2 at different climate conditions during January 2015 to December 2015. Respirable Dust Sampler (RDS) and Fine Particulate Sampler were used to determine PM10 and PM2.5 respectively. Findings: The present work shows its significance because there is no such data available for researchers for reference. Industrial (foundry units) and automobile emissions are the major pollution source which determines the quality of ambient air in the city. Among the different climatic conditions, summer exhibits more pollution level in the air while monsoon exhibits less pollution level. Results also reveals that the Industrial area experiencing more pollutants followed by commercial and residential areas. As per the AQI category, all locations are coming under moderate category except industrial location which requires stringent control measures to reduce the particulate matters in the air. Applications: Since no data is available for the Air Quality Index study of the region, this can be taken as a reference for future study.

Northeast China has been reported as having serious air pollution in China with increasing occurrences of severe haze episodes. Changchun City, as the center of Northeast China, has longstanding industry and is an important agricultural base. Additionally, Changchun City has a long winter requiring heating of buildings emitting pollution into the air. These factors contribute to the complexity of haze pollution in this area. In order to analyze the causes of heavy haze, surface air quality has been monitored from 2013 to 2015. By using satellite and meteorological data, atmospheric pollution status, spatio-temporal variations and formation have been analyzed. Results indicated that the air quality in 88.9% of days exceeding air quality index (AQI) level-1 standard (AQI >50) according to the National Ambient Air Quality Standard (NAAQS) of China. Conversely, 33.7% of the days showed a higher level with AQI > 100. Extreme haze events (AQI > 300) occurred frequently during agricultural harvesting period (from October 10 to November 10), intensive winter heating period (from Late-December to February) and period of spring windblown dust (April and May). Most daily concentrations of gaseous pollutants, i.e., NO2 (43.8 μg/m3), CO (0.9 mg/m3), SO2 (37.9 μg/m3), and O3 (74.9 μg/m3) were evaluated within level-1 concentration limits of NAAQS standards. However, particulate matter (PM2.5 and PM10) concentrations (67.3 μg/m3and 115.2 μg/m3, respectively) were significantly higher than their level-1 limits. Severe haze in spring was caused by offsite transported dust and windblown surface soil. Heavy haze periods during fall and winter were mainly formed by intensive emissions of atmospheric pollutants and steady weather conditions (i.e., low wind speed and inversion layer). The overlay emissions of widespread straw burning and coal combustion for heating were the dominant factors contributing to haze in autumn, while intensive coal burning during the coldest time was the primary component of total emissions. In addition, general emissions including automobile exhaust, road and construction dust, residential and industrial activities, have significantly increased in recent years, making heavy haze a more frequent occurrence. Therefore, both improved technological strategies and optimized pollution management on a regional scale are necessary to minimize emissions in specified seasons in Changchun City, as well as comprehensive control measures in Northeast China.

Ambient air quality is one of the most serious environmental concerns in urban areas around the world, especially in developing countries. In recent years, there has been increasing concern over the adverse effects of air pollution on human health. In the present study, the concentrations of air pollutants and their adverse effect at various locations over Delhi are studied using fuzzy air quality index (FAQI). The 24-hourly average concentrations of main criteria pollutants viz., fine particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO) and ozone (O3) over the years 2008 to 2012 have been used for the analysis at three different locations in Delhi (residential-Sirifort, industrial-DCE and busiest traffic intersection-ITO junction). Further, the weightages of criteria pollutants have been determined using an analytical hierarchical process to access the air quality index. The developed methodologies also have been compared with the conventional approach and validated with different statistical measures. The results clearly indicate that the air quality of Delhi is degraded and lies in the ranges of 'bad' and 'very bad' categories.

Spatial distribution and health implications of particulate matter concentrations across diverse land use types in Dinajpur District, Bangladesh

The impact of coastal developmental projects such as ports on regional air quality has been a matter of concern, as they are known to induce rapid industrialization and urbanization as collateral development on a long-term basis. Gandhidham-Kachchh, Gujarat, India is hub for industrialization, thus various developmental activities and port activities may release toxic gases into the environment. Thus, an attempt was made to assess the present ambient air quality in and around eastern coastal region of Gandhidham, Gujarat using Air Quality Index (AQI) and Air Quality Health Index (AQHI). The primary focus was on respirable particulate matter (PM10) and fine particulate matter (PM2.5) along with other gaseous pollutants. In total 134 samples were collected from 11 stations during January to December 2018. Sampling was carried out 24 hourly on a monthly basis. Respirable particulate matter (PM10), fine particulate matter (PM2.5), sulphur dioxide and nitrogen dioxide ranged from 29-401 µg/m3, 13-184 µg/m3, 5.9-92.6 µg/m3and 3.7-33 µg/m3, respectively. The annual mean concentrations varied from 118-227 µg/m3, 47-82 µg/m3, 10-51 µg/m3, 11-41 µg/m3 for PM10, PM2.5, sulphur dioxide and nitrogen dioxide, respectively. In most of the sampling locations both the particulate matter fractions exceeded the permissible limit as prescribed in National Ambient Air Quality Standard (NAAQS). The AQI ranged from 28 to 106, which falls under Good and Satisfactory category of pollution as per the Central Pollution Control Board (CPCB) standards except at A-11 which falls under Moderate category. The AQHI was in the range of 5.9 to 15.8, which was alarming indicating Moderate to Very Higher Risk to the people. The present findings throw light on the prevailing scenario in the ambient air quality of the studied region, thereby suggesting for strict implementation and thorough control measures for reduction of particulate matters in the air.

Background: Emerging evidence suggests that air pollution is a significant but under recognized contributor to cardiovascular disease (CVD) mortality, even in middle age adults. This study evaluates the association between long term statewise air quality, as measured by median Air Quality Index (AQI) and cardiovascular mortality rates among adults aged 35–44 years across the United States over a 25-year period. Methods: We linked annual statewise median AQI data from EPA AQS (2000–2024) with CDC WONDER mortality records for non-infectious CVD in the 35–44 age group. States were ranked by mean AQI to define “Highest 10” and “Lowest 10” AQI cohorts. Mean annual cardiovascular crude death rates were compared between these groups using t-tests, and trends were visualized year-by-year. Correlation and linear regression analyses assessed the association between AQI and mortality across all state years as well. Results: States with the highest long-term AQI (poor air quality) had significantly higher mean cardiovascular mortality rates (mean 46.99 per 100,000) than states with the lowest AQI (good air quality) (mean 30.35 per 100,000; p = 0.01). Yearwise analysis showed a persistent gap in mortality rates with the highest AQI states consistently exhibiting greater CVD mortality across the study period. Linear regression confirmed a significant positive association between AQI and mortality (slope = 0.62, p < 0.001), although AQI alone explained only a modest portion of the variation (R 2 = 0.095). Conclusions: Higher long term air pollution exposure, as measured by AQI, is associated with increased cardiovascular mortality among U.S. adults aged 35–44 years. This relationship is robust at the extremes of air quality and persists over time. This highlights the need for public health policies to improve air quality as a means to reduce early onset CVD mortality. Further studies should explore mechanisms and policy interventions to mitigate this risk.

The current study aims to characterize and estimate the particulate matter (PM) exposure level of the population near an opencast coal mine. PM concentrations of different sizes were monitored at 32 locations distributed uniformly in the study area for spatial profiling and at 1 location for analysing the temporal variations and characterization of dust. Health impacts of PM to nearby residents were estimated using the air quality index (AQI) values and were cross-validated using the health survey data. The monitoring results indicated that the 15 min average concentrations of PM10 and PM2.5 were 2.3–2.4 times of the corresponding 24 h National Ambient Air Quality Standards (NAAQS) in all the monitoring stations. Low wind speed during monitoring campaign that resulted in little PM dispersion is suggested to be the reason for this. AQI levels at five villages surrounding the mine were “moderate to severe” during morning-hours that improved to “satisfactory to moderate” during day-time. Eye irritation and respiratory problems were the two most adverse effects experienced by the residents living nearby to the mine. The health survey data and the corresponding AQI values indicate that respiratory problem is well correlated with the AQI values. The dust contains mainly six major elements in the order of O > C > F > Si > Al > Fe > Ca, accounting for 95% of the weight percentages of the 20 elements for which the characterization was carried out.

Concerning ecological integrity, public health, and climate change, air quality is a crucial environmental concern. In emerging nations like India, in particular, rapid industrialization and urbanization have had a major negative impact on the quality of the air. Not an exception to this tendency is Meerut, a well-known city in the state of Uttar Pradesh. This study compares data from various locations, including industrial areas, commercial areas, residential areas, and roadside, to identify trends and potential sources of air pollution. It then evaluates the air quality at a few selected areas in Meerut City. The sampling of the air was performed monthly starting from January 2024 to June 2024 using the high-volume respirable dust sampler (Envirotech-ETC APM 415 BL) with a gaseous attachment. The study focuses on major air pollutants that are known to have negative impacts on both human health and the ecosystem, such as carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter (PM10 and PM2.5). The data was processed to calculate the Air Quality Index (AQI) to rank the studied sites based on pollution and to make the data understandable to the common people. The obtained results revealed that at all the studied sites, values of PM10 (107.0-116.6 µg/m³) were found beyond the standard values of National Ambient Air Quality Standards (NAAQ) values of 100 µg/m³, and the values of the rest of the analyzed parameters {PM2.5 (50.6-59.4 µg/m³), NO₂ (29.7-33.1 µg/m³), SO₂ (9.1-10.3 µg/m³), and CO (0.9 µg/m³)} were found within the NAAQ standard. Among all the parameters, PM10 was considered as the criteria pollutant because the subindex (Si) of PM10 (1.07-1.17) was found highest at all the sites. The subindex (Si) of CO (0.22-0.95) was found to be the lowest among all the studied parameters. Based on the AQI, we found the overall quality of air in the study area to be moderately polluted.

Air pollution has posed a serious public health issue in China. In the study, we aimed to examine the burden of air pollution and its association with climate factors and total mortality. City-level daily air quality index (AQI) data in 161 cities of China in 2014, and meteorological factors, socioeconomic status and total morality were obtained from China environmental, meteor-ology and healthcare agencies. Linear regression, spatial autocorrelation analysis and panel fixed models were applied in data analysis. Among 161 cities, monthly average AQI was significantly different by seasons and regions. The highest average AQI was in winter, and the lowest in summer. A significant clustering distribution of AQI by cities was observed, with the highest AQI in north China (22 cities, mean = 117.36). Among the 161 cities, 5 cities (3%) had AQI > 150 (e.g., moderate polluted reference value), and 50 cities (31.1%) had AQI between 100 and 150 (slightly polluted value). Heat index, precipitation and sunshine hours were negatively and significantly, but air pressure was positively correlated with AQI. Cities with higher AQI concentrations had higher total mortality than those with lower AQI. This AQI-mortality association remained significant after adjustment for socioeconomic status. In conclusion, the study highlights the burden and seasonal, regional and areas variations in air pollution across the nation. Air pollution is estimated to account for more than 4% of the urban health inequality in total mortality in China.

Air pollution and its associated health risks before and after COVID-19 in Shaanxi Province, China

With the continuous progress of human production and life, air quality has become the focus of attention. In this paper, Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces were taken as the study area, where there are 58 air quality monitoring stations from which daily and monthly data are obtained. Firstly, the temporal characteristics of the air quality index (AQI) are explored. Then, the spatial distribution of the AQI is mapped by the inverse distance weighted (IDW) method, the ordinary kriging (OK) method and the Bayesian maximum entropy (BME) method. Additionally, cross-validation is utilized to evaluate the mapping results of these methods with two indexes: mean absolute error and root mean square interpolation error. Furthermore, the correlation analysis of meteorological factors, including precipitation anomaly percentage, precipitation, mean wind speed, average temperature, average water vapor pressure and average relative humidity, potentially affecting the AQI was carried out on both daily and monthly scales. In the study area and period, AQI shows a clear periodicity, although overall, it has a downward trend. The peak of AQI appeared in November, December and January. BME interpolation has a higher accuracy than OK. IDW has the maximum error. Overall, the AQI of winter (November), spring (February) is much worse than summer (May) and autumn (August). Additionally, the air quality has improved during the study period. The most polluted areas of air quality are concentrated in Beijing, the southern part of Tianjin, the central-southern part of Hebei, the central-northern part of Henan and the western part of Shandong. The average wind speed and average relative humidity have real correlation with AQI. The effect of meteorological factors such as wind, precipitation and humidity on AQI is putative to have temporal lag to different extents. AQI of cities with poor air quality will fluctuate greater than that of others when weather changes and has higher correlation with meteorological factors.