An assessment of ambient air quality using AQI and exceedance factor for Udaipur City, Rajasthan (India)

Air pollution is a severe issue of concern worldwide due to its most significant environmental risk to human health today. All substances that appear in excessive amounts in the environment, such as PM10, NO2, or SO2, may be associated with severe health problems. Anthropogenic sources of these pollutants are mainly responsible for the deterioration of urban air quality. These sources include stationary point sources, mobile sources, waste disposal landfills, open burning, and similar others. Due to these pollutants, people are at increased risk of various serious diseases like breathing problems and heart disease, and the death rate due to these diseases can also increase. Hence, air quality monitoring is essential in urban areas to control and regulate the emission of these pollutants to reduce the health impacts on human beings. Udaipur has been selected for the assessment of air quality with monitored air quality data. Air quality monitoring stations in Udaipur city are operated by the CPCB (Central Pollution Control Board) and RSPCB (Rajasthan State Pollution Control Board). The purpose of this study is to characterize the level of urban air pollution through the measurement of PM10, NO2, or SO2 in Udaipur city, Rajasthan (India). Four sampling locations were selected for Udaipur city to assess the effect of urban air pollution and ambient air quality, and it was monitored for a year from 1st January 2019 to 31st December 2019. The air quality index has been calculated with measured values of PM10, NO2, and SO2. The concentration of PM10 is at a critical level of pollution and primarily responsible for bad air quality and high air quality Index in Udaipur city.

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Methodological Approaches to Optimizing Ambient Air Quality Monitoring Programs within the Framework of the Federal Clean Air Project (on the example of Nizhny Tagil)

ЗДОРОВЬЕ НАСЕЛЕНИЯ И СРЕДА ОБИТАНИЯ - ЗНиСО / PUBLIC HEALTH AND LIFE ENVIRONMENT ◽

10.35627/2219-5238/2020-330-9-38-47 ◽

2020 ◽

pp. 38-47

Author(s):

VB Gurvich ◽

DN Kozlovskikh ◽

IA Vlasov ◽

IV Chistyakova ◽

SV Yarushin ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Health Risks ◽

Monitoring Program ◽

Ambient Air ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Ambient Air Quality ◽

Clean Air ◽

Methodological Approaches

ntroduction: One of the key socially significant results of implementing the Federal Clean Air Project is the maximum possible mitigation of inhalation health risks by achieving the target rate of reducing emissions (by 20 % against the level of 2017) in a number of cities included in the federal project as priority areas. Materials and methods: Ambient air pollution monitoring as a measure of this accomplishment is indispensable both for verification of applying the model to estimating surface concentrations of pollutants, assessing health risks, and evaluating changes in ambient air quality. For the objectivity of such assessments, it is fundamental to determine the list of priority health-threatening air pollutants, to select monitoring sites that best characterize population exposure to these chemicals, and to plan air quality monitoring programs setting sampling frequency and volume. Results: The article presents the results of implementing methodological approaches adopted by the Russian Federal Service for Surveillance in the Sphere of Consumer Rights and Human Wellbeing (Rospotrebnadzor) to optimize ambient air quality monitoring within the framework of solving the tasks of the Federal Clean Air Project in the city of Nizhny Tagil, Sverdlovsk Region, in 2019. The Nizhny Tagil air quality monitoring program for 2020 has been developed and tested. This program, in conjunction with similar programs carried out by the Russian Federal Hydrometeorology and Environmental Monitoring Service (Roshydromet) and the Ministry of Natural Resources and Environment of the Sverdlovsk Region and taking into account their implementation over the past five years, helps provide implementers of the federal project with air pollution data to address its key challenges. Conclusions: The adopted ambient air quality monitoring program implemented in Nizhny Tagil in 2020 by the Center for Hygiene and Epidemiology in the Sverdlovsk Region meets terms and requirements of the Federal Clean Air Project.

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Design of air quality monitoring network of Luanda, Angola: Urban air pollution assessment

Atmospheric Pollution Research ◽

10.1016/j.apr.2021.101128 ◽

2021 ◽

pp. 101128

Author(s):

Pascoal M.D. Campos ◽

Ana F. Esteves ◽

Anabela A. Leitão ◽

José C.M. Pires

Keyword(s):

Air Pollution ◽

Air Quality ◽

Urban Air Pollution ◽

Monitoring Network ◽

Pollution Assessment ◽

Quality Monitoring ◽

Urban Air ◽

Air Quality Monitoring ◽

Air Pollution Assessment

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Influences of Season and Geography on Urban Air Quality Monitoring Stations (AQMS) Representativeness Areas by High Resolution Air Quality Monitoring

10.5194/egusphere-egu2020-13652 ◽

2020 ◽

Author(s):

Tilman Leo Hohenberger

Keyword(s):

Air Pollution ◽

Hong Kong ◽

Air Quality ◽

High Resolution ◽

Urban Air Pollution ◽

Quality Monitoring ◽

Urban Air ◽

Air Quality Monitoring ◽

Air Quality Monitoring Stations ◽

Better Than

Urban air pollution remains a key pressure on public health. With the megatrend of urbanization and its forcing on emissions and exposure, effective monitoring tools in cities are at the center of prevention efforts.Air Quality Monitoring Stations (AQMS) are traditionally used for regulatory efforts and, increasingly, as publicly available information sources. Facing high levels of air pollution heterogeneity in complex urban environments, a simple spatial approach is often misleading when choosing an AQMS that represents local street-level conditions the best. Model-based calculation of representativeness areas are rare for the urban scale (e.g. Rodriguez et al., 2019), and suffer from short model times, low model correlations and a lack of external validation by observation data. Moreover, as both health impacts and air-pollution episodes are influenced by environmental factors, the sensitivity of representativeness areas to wind impacts and during different seasons are a further point of interest not covered well by previous literature.For the high-density environment of geographically complex Hong Kong, we used a full year (2019) of high-resolution air quality modelling (ADMS-Urban) data to establish representativeness areas for the territory&#8217;s 16 AQMS. We constructed representativeness areas for key air-pollutants for the full period and based on season and wind speed. We parameterized the effects of wind and geography on the size and shape of the representativeness areas. Furthermore, we validated our findings by a series of week-long outdoor measurements aimed to cover the whole territory of Hong Kong.Our results show that Hong Kong&#8217;s AQMS network covering the territory well for a PM2.5, PM10 and O3, where the mean CSF (hourly Concentration Similarity Frequency with a target of &#177;20%) of each grid-cell to the best matching AQMS lies at around 60%. Both NO2 and SO2 are less well represented, with a CSF of around 30%. Moreover, we show that representativeness areas calculated from similarity-based metrices as CSF and percentage difference represent the impact of geographical features on pollution dispersion better than correlation-based metrices (R2 and ioa). It was further found that AQMS represent upwind areas better than downwind areas, especially in areas exposed to open wind-flow, and that the represented areas change strongly over the course of a year.In this study, we showcase the ability of high-resolution urban air-pollution modelling to guide the public with information on AQMS representativeness. Furthermore, we report that representativeness areas are non-static, changing with seasons and under the influence of wind. High-resolution urban modelling can further be used to gauge the quality of AQMS networks and assess the need and location of additions to an existing network.&#160;Rodriguez, D., Valari, M., Payan, S., & Eymard, L. (2019). On the spatial representativeness of NOX and PM10 monitoring-sites in Paris, France. Atmospheric Environment: X, 1, 100010.

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Visualization and Interpretation of PM10 Monitoring Data Related to Causes of Haze Episodes in Northern Thailand

Applied Environmental Research ◽

10.35762/aer.2015.37.2.3 ◽

2015 ◽

pp. 33-48 ◽

Cited By ~ 3

Author(s):

Sirapong Sooktawee ◽

Usa Humphries ◽

Aduldech Patpai ◽

Rungrawee Kongsong ◽

Suteera Boonyapitak ◽

...

Keyword(s):

Air Quality ◽

Pollution Control ◽

Ambient Air ◽

Quality Monitoring ◽

Monitoring Data ◽

Air Quality Monitoring ◽

Ambient Air Quality ◽

Related Factors ◽

Northern Thailand ◽

Control Department

Monitoring of ambient air quality yields data typically presented as time series plots, tables of summarized statistical values, or other representations. This paper presents an alternative way to visualizing air quality monitoring data by presenting concentrations in the form of a calendar, offering a familiar way for reader to identify air quality trends on various time scales(daily, weekly, or monthly). One of the major air pollution problems in the northern part of Thailand is haze, which is related tothe concentration of airborne particulates less than 10 microns in size (PM10). This paper presents calendars of PM10 concentrations monitored by the Pollution Control Department across northern Thailand. Hourly mean PM10 concen-trationsmonitored at 13 stations were used to construct PM10 concentration calendars for each station. Haze episodes are clearly identifiable in the visualization; the calendar also allows easy comparison of PM10 levels between years. We also observed the absenceof any haze episodes in 2011, and propose possible related factors.

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Assessment of Air Pollution status during COVID-19 Lockdown (Mar-May 2020) over Bangalore City in India

10.21203/rs.3.rs-166736/v1 ◽

2021 ◽

Author(s):

K C Gouda ◽

Priya Singh ◽

P Nikhilasuma ◽

Mahendra Benke ◽

Reshama Kumari ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Pollution Control ◽

Air Pollutants ◽

Ambient Air ◽

Ambient Air Quality ◽

Control Board ◽

Ambient Concentration ◽

The City ◽

Concentration Of Air Pollutants

Abstract The Coronavirus disease 2019 (COVID-19), which became a global pandemic by March 2020 (WHO, 2020), forced almost all countries over the world to impose the lockdown as a measure of social distancing to control the spread of infection. India also strictly implemented a countrywide lockdown, starting from 24th March onwards. This measure resulted in the reduction of the sources of air pollution in general; industrial, commercial, and vehicular pollution in particular, with visible improvement in Ambient Air Quality. In this study, the impact of COVID-19 lockdown on the ambient concentration of air pollutants over the city of Bengaluru (India) is assessed using Continuous Ambient Air Quality Measurement (CAAQM) data from 10 monitoring stations spread across the city. The data was obtained from Central Pollution Control Board (CPCB) and Karnataka State Pollution Control Board (KSPCB). The analysis of the relative changes in the ambient concentration of six major air pollutants (NO, NO2, NOX, PM2.5, O3, and SO2) been carried out for two periods; March-May 2020 (COVID-19 lockdown) and the corresponding period of 2019 which was Non-COVID. The analysis revealed significant reduction in the concentration of ambient air pollutants at both daily and monthly intervals. This can be attributed to the reduction in sources of emission; vehicular traffic, industrial, and other activities. The average reduction in the concentration of NO, NO2, NOX, PM2.5, and O3 between 1st March to 12th May 2020 was found to be 63%, 48%, 48%, 18%, and 23% respectively when compared to the same period in 2019. Similarly, the comparative analysis of pollutant concentrations between pre-lockdown (March 01- March 23) and lockdown (Mar 24-May 12) period, shown a huge reduction in the ambient concentration of air pollutants; 47.3% (NO), 49% (NO2), 49% (NOX), 10% (SO2), 37.7% (PM2.5), and 15.6% (O3), resulting in improved air quality over Bangalore during the COVID-19 lockdown period. It is shown that the strict lockdown resulted in a significant reduction in the pollution levels. Such lockdowns may be useful as emergency intervention strategies to control air pollution in megacities when ambient air quality deteriorates dangerously.

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