Assessment and Spatial Distribution Mapping of Criteria Pollutants

Kamboj, Kuldeep; Sisodiya, Sohil; Mathur, Abhishek; Zare, Ali; Verma, Puneet

doi:10.1007/s11270-022-05522-y

Cited by 16 publications

(6 citation statements)

References 23 publications

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“…On an opposite situation, a city in China had a good air quality (lower AQI) during summer than winter season 22 . In a study using mapping of AQI, season (winter vs. summer) did not vary and also resulted to a similar trend despite of rainfall variabilities 19 . Factors affecting PM10 concentration and distribution can be influenced by factors primarily with air pollution sources and intensity of emission as well as meteorological condition (e.g.…”

Section: Resultsmentioning

confidence: 81%

“…Close to these two stations exists a school (Calinan National High School) and a commercial establishment (Toril Plaza) may influence PM10 emission sources. In India, PM10 had a major contribution relative to NO2 and SO2 to the overall AQI level as concentration continuously increase in areas with high industrial activities and where educational institutions were established 19 . PM10 concentration of a central business district in the country significantly contribute to the local surface dust measured 20 .…”

Section: Resultsmentioning

confidence: 99%

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Particulate matter concentration in Davao City airshed and its trend over the years

Anunciado

2023

Preprint

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Davao City airshed was selected for air quality mapping using particulate matter (PM) concentrations. PM data were taken from the regulatory office, Environmental Management Bureau XI, from 2016 to 2021 to understand annual variation and determine trends that may be attributed to seasonal changes in the region. PM concentrations were spatially interpolated using Inverse distance weighting (IDW) feature, an interpolation technique of ArcGIS. PM concentration and distribution over the years showed no similar patterns, both for PM10and PM2.5. No annual similarities of PM concentration were observed, and distribution varies yearly. No seasonal trends were shown on the interpolated maps for PM. However, there was an overall PM concentration decrease and distribution covered fewer affected areas over time. PM concentration in 2016 were generally at a level within the defined limit of NAAQGV except for some AQMS locations and years but sparingly exceeding the NAAQGV limit over time. Results show that PM emissions were lower suggesting a possible success on the regulation policies in the Davao City airshed through reduction or better management of air pollutant emissions.

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Section: Resultsmentioning

confidence: 81%

Section: Resultsmentioning

confidence: 99%

Particulate matter concentration in Davao City airshed and its trend over the years

Anunciado

2023

Preprint

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“…This process uses four sampling points, including nine estimated air humidity on each point, totally the IDW used thirty-six points. The consideration of using IDW is based on much research on predicting the spatial distribution that mentioned the IDW (Jumaah et al, 2019;Kamboj et al, 2022;Nguyen et al, 2020). The formula for this process is available below (Eq.5-7).…”

Section: Interpolating Estimated Air Humiditymentioning

confidence: 99%

Mapping the Air Moisture Change in Under Canopy Trees Using a Hemispherical and Aerial Photograph Based on Machine Learning Approaches

Ghazali

2023

IJReSES

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The essential roles of trees in controlling the local climatic variation, such as air moisture, are still interesting to observe. Therefore, this study must deliver knowledge of the benefits of growing trees and enhance people's awareness of climate change adaptation. Here, the analysis requires several data fields such as hemispherical photography, an aerial photograph of a UAV, and air temperature collected using a wet and dry bulb thermometer, which has converted to air moisture. All these are considered to understand the air moisture change under the trees' canopy during a day observation. The hemispherical photography and aerial photograph of a UAV are processed to measure the tree's canopy size and then used together with interpolated air moisture to map the variation in air moisture distribution in under-canopy trees using random forest (RF) and Artificial Neural Network (ANN). The result shows that hemispherical photography describes the ability to control the air moisture change. As its size increases, the air moisture level tends to be higher. It was maintained at more than 70% compared to the area with lower canopy cover. This characteristic is similar to the pattern shown by the RF and ANN. However, the SVM has better results as it can separate air humidity in vegetated and non-vegetated areas.

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“…Past studies on the Kota metropolis did not quantify the effect of meteorological parameters on the city. 44,45 Hence, a prediction model has been developed using multiple regression analysis with a confidence level of 95%. This study may be beneficial for stakeholders and policymakers to consider the effect of meteorological factors on the ambient air of Kota city to make new policies and rules to curb air pollution.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Relationship Between Meteorological Parameters and Air Quality Index of Kota, Rajasthan, India

Mathur

Kamboj

2023

Curr. World Environ

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Meteorological parameters extremely influence the air quality of metropolitan cities worldwide. This study analyses the impact of meteorological variables on the quality of air for the Kota metropolis of Rajasthan (India) from 2018 to 2021 for four years. Wind speed and direction, relative humidity, precipitation, and temperature are the meteorological parameters selected to statistically determine the effect of meteorological variables on the AQI (air quality index). The AQIs are evaluated through PM10, PM2.5, NO2, and SO2. The average concentration of PM10 was 122.59 ± 44.11µg/m3, PM2.5 was 56.83 ± 24.89 µg/m3, NO2 was 24.91 ± 4.99 µg/m3, and SO2 was 7.39 ± 1.38 µg/m3 during the observation period. The average temperature varied between 18-38 °C in 2018, 15.7-34.8 °C in 2019, 15.5-35.1 in 2020, and 16.8-32.4 °C. Total rainfall in 2018, 2019, 2020, and 2021 was 685 mm, 1637 mm, 514 mm, and 1338 mm, respectively. The average wind speed in the city were 1.39, 1.30, 1.26, and 1.22 m/s in 2018, 2019, 2020, and 2021, respectively. The relative humidity ranged from 17-78 % in 2018, 22-84% in 2019, 40-90 % in 2020, and 24-82 % in 2021. The annual average AQI of Kota was 139, 118, 101, and 142 in 2018, 2019, 2020, and 2021, respectively. There is a statistically significant correlation between temperature and AQI (p<0.05), rainfall and AQI (p<0.05), and wind speed and AQI (p<0.05), which show a significant impact on the air quality of the Kota metropolis. At the same time, there is a statistically insignificant correlation between relative humidity and AQI (p>0.05), which shows an insignificant impact on air quality. The formulated equation for predicting AQI through meteorological parameters has wide scope utility in air pollution management and control.

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Assessment and Spatial Distribution Mapping of Criteria Pollutants

Cited by 16 publications

References 23 publications

Particulate matter concentration in Davao City airshed and its trend over the years

Particulate matter concentration in Davao City airshed and its trend over the years

Mapping the Air Moisture Change in Under Canopy Trees Using a Hemispherical and Aerial Photograph Based on Machine Learning Approaches

Assessment of Relationship Between Meteorological Parameters and Air Quality Index of Kota, Rajasthan, India

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