The representativeness of ground-based air quality monitoring stations: observation and recommendation from Indian cities

<div> <p>It is well established that the high level of particulate matter is a leading cause of premature mortality and disease worldwide and especially in South Asia (Global Burden of Disease Study, 2019). The ground-based air quality (AQ) monitoring stations are used&#160;to calculate economic loss, premature mortality and validate the conversed PM2.5&#160;concentration from satellite-based Aerosol Optical Depth (AOD) data. Over India, 793 manual monitoring air quality (AQ) monitoring stations and 307 automated AQ monitoring station are presently operating under the aegis of National Air Quality Monitoring&#160;Programme&#160;and Central Pollution Control Board respectively. However, studies addressing the spatial representativeness of the data generated from the AQ monitoring stations over India are very limited and therefore, it is unclear that whether the existing stations are sufficient to reflect the average ambient AQ over different Indian cities.&#160;</p> </div><div> <p>The present study intends to classify the existing AQ monitoring stations on the basis of spatial representativeness and derive a general conceptual framework for commissioning representative AQ monitoring sites for Indian cities. The methodology involves analysis of land use, populations and air quality data for the existing air quality stations in million plus Indian cities. A case study was conducted for Pune (18.5&#176; N, 73.8&#176; E), a western Indian metro city with 3.15 million population (Census, 2011). Using the night-time light data and high resolution PM2.5, population exposure hotspots over Pune city were identified. It was observed that not only at the midst of the municipal area, population exposure hotspots can be identified at the peripheral region of PMC/PNMC which certainly signify the role of rapid developmental activity and urban agglomeration over Pune city. The existing air quality monitoring sites are located majorly in the pollution hotspots in the city center region and therefore installing AQ monitoring stations (co-located&#160; with weather station) at the rapidly developing parts of the city is highly recommended. The present land use pattern and the location of existing monitoring sites suggests lack of urban background monitoring stations which indicates the gap of knowledge in monitoring the average air quality responsible of long-term health effect over Pune. The prevalence of AQ monitoring stations in the road junction points and near to metro construction works might overestimate the exposure estimate of the general population in the city.&#160;&#160;&#160;</p> </div>

Download Full-text

A Cluster Analysis of PM2.5 Using CMAQ Model Results for Representativeness of Air Quality Monitoring Networks in Busan, Korea

10.5194/egusphere-egu2020-3189 ◽

2020 ◽

Author(s):

Woo-Sik Jung ◽

Woo-Gon Do

Keyword(s):

Air Pollution ◽

Cluster Analysis ◽

Air Quality ◽

Monitoring Network ◽

Hierarchical Cluster ◽

Quality Monitoring ◽

Quality Data ◽

Air Quality Monitoring ◽

Monitoring Networks ◽

Monitoring Stations

<p><strong>With increasing interest in air pollution, the installation of air quality monitoring networks for regular measurement is considered a very important task in many countries. However, operation of air quality monitoring networks requires much time and money. Therefore, the representativeness of the locations of air quality monitoring networks is an important issue that has been studied by many groups worldwide. Most such studies are based on statistical analysis or the use of geographic information systems (GIS) in existing air quality monitoring network data. These methods are useful for identifying the representativeness of existing measuring networks, but they cannot verify the need to add new monitoring stations. With the development of computer technology, numerical air quality models such as CMAQ have become increasingly important in analyzing and diagnosing air pollution. In this study, PM2.5 distributions in Busan were reproduced with 1-km grid spacing by the CMAQ model. The model results reflected actual PM2.5 changes relatively well. A cluster analysis, which is a statistical method that groups similar objects together, was then applied to the hourly PM2.5 concentration for all grids in the model domain. Similarities and differences between objects can be measured in several ways. K-means clustering uses a non-hierarchical cluster analysis method featuring an advantageously low calculation time for the fast processing of large amounts of data. K-means clustering was highly prevalent in existing studies that grouped air quality data according to the same characteristics. As a result of the cluster analysis, PM2.5 pollution in Busan was successfully divided into groups with the same concentration change characteristics. Finally, the redundancy of the monitoring stations and the need for additional sites were analyzed by comparing the clusters of PM2.5 with the locations of the air quality monitoring networks currently in operation.</strong></p><p><strong>This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A3B03036152).</strong></p>

Download Full-text

VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan

Atmosphere ◽

10.3390/atmos11101096 ◽

2020 ◽

Vol 11 (10) ◽

pp. 1096

Author(s):

Edward Ming-Yang Wu ◽

Shu-Lung Kuo

Keyword(s):

Time Series ◽

Air Quality ◽

Air Pollutants ◽

Quality Monitoring ◽

Quality Data ◽

Air Quality Monitoring ◽

Egarch Model ◽

Southern Taiwan ◽

Air Quality Monitoring Stations ◽

Monitoring Stations

This study adopted the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to analyze seven air pollutants (or the seven variables in this study) from ten air quality monitoring stations in the Kaohsiung–Pingtung Air Pollutant Control Area located in southern Taiwan. Before the verification analysis of the EGARCH model is conducted, the air quality data collected at the ten air quality monitoring stations in the Kaohsiung–Pingtung area are classified into three major factors using the factor analyses in multiple statistical analyses. The factors with the most significance are then selected as the targets for conducting investigations; they are termed “photochemical pollution factors”, or factors related to pollution caused by air pollutants, including particulate matter with particles below 10 microns (PM10), ozone (O3) and nitrogen dioxide (NO2). Then, we applied the Vector Autoregressive Moving Average-EGARCH (VARMA-EGARCH) model under the condition where the standardized residual existed in order to study the relationships among three air pollutants and how their concentration changed in the time series. By simulating the optimal model, namely VARMA (1,1)-EGARCH (1,1), we found that when O3 was the dependent variable, the concentration of O3 was not affected by the concentration of PM10 and NO2 in the same term. In terms of the impact response analysis on the predictive power of the three air pollutants in the time series, we found that the asymmetry effect of NO2 was the most significant, meaning that NO2 influenced the GARCH effect the least when the change of seasons caused the NO2 concentration to fluctuate; it also suggested that the concentration of NO2 produced in this area and the degree of change are lower than those of the other two air pollutants. This research is the first of its kind in the world to adopt a VARMA-EGARCH model to explore the interplay among various air pollutants and reactions triggered by it over time. The results of this study can be referenced by authorities for planning air quality total quantity control, applying and examining various air quality models, simulating the allowable increase in air quality limits, and evaluating the benefit of air quality improvement.

Download Full-text

Short-term environmental impact of ambient air quality trends in during the COVID-19 pandemic in India

Annals of Civil and Environmental Engineering ◽

10.29328/journal.acee.1001031 ◽

2021 ◽

Vol 5 (1) ◽

pp. 017-025

Author(s):

Karuppasamy Manikanda Bharath ◽

Natesan Usha ◽

Periyasamy Balamadeswaran ◽

S Srinivasalu

Keyword(s):

Air Quality ◽

Ambient Air ◽

Air Pollutant ◽

Quality Monitoring ◽

Quality Data ◽

Air Quality Monitoring ◽

The North ◽

Air Quality Monitoring Stations ◽

Air Quality Data ◽

Monitoring Stations

The lockdown, implemented in response to the COVID-19 epidemic, restricted the operation of various sectors in the country and its highlights a good environmental outcome. Thus, a comparison of air pollutants in India before and after the imposed lockdown indicated an overall improvement air quality across major Indian cities. This was established by utilizing the Central Pollution Control Board’s database of air quality monitoring station statistics, such as air quality patterns. During the COVID-19 epidemic, India’s pre-to-post nationwide lockdown was examined. The air quality data was collected from 30-12-2019 to 28-04-2020 and synthesized using 231 Automatic air quality monitoring stations in a major Indian metropolis. Specifically, air pollutant concentrations, temperature, and relative humidity variation during COVID-19 pandemic pre-to-post lockdown variation in India were monitored. As an outcome, several cities around the country have reported improved air quality. Generally, the air quality, on a categorical scale was found to be ‘Good’. However, a few cities from the North-eastern part of India were categorized as ‘Moderate/Satisfactory’. Overall, the particulate matters reduction was in around 60% and other gaseous pollutants was in 40% reduction was observed during the lockdown period. The results of this study include an analysis of air quality data derived from continuous air quality monitoring stations from the pre-lockdown to post-lockdown period. Air quality in India improved following the national lockdown, the interpretation of trends for PM 2.5, PM 10, SO2, NO2, and the Air Quality Index has been provided in studies for major cities across India, including Delhi, Gurugram, Noida, Mumbai, Kolkata, Bengaluru, Patna, and others.

Download Full-text

Land use to characterize spatial representativeness of air quality monitoring stations and its relevance for model validation

Atmospheric Environment ◽

10.1016/j.atmosenv.2012.05.028 ◽

2012 ◽

Vol 59 ◽

pp. 492-500 ◽

Cited By ~ 27

Author(s):

Stijn Janssen ◽

Gerwin Dumont ◽

Frans Fierens ◽

Felix Deutsch ◽

Bino Maiheu ◽

...

Keyword(s):

Land Use ◽

Air Quality ◽

Model Validation ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Air Quality Monitoring Stations ◽

Monitoring Stations

Download Full-text

Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽

10.3390/s21103338 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3338

Author(s):

Ivan Vajs ◽

Dejan Drajic ◽

Nenad Gligoric ◽

Ilija Radovanovic ◽

Ivan Popovic

Keyword(s):

Machine Learning ◽

Air Quality ◽

Relative Humidity ◽

Low Cost ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Lower Accuracy ◽

Wide Range ◽

The Impact ◽

Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

Download Full-text