scholarly journals ENABLING AIR QUALITY MONITORING WITH THE OPEN DATA CUBE: IMPLEMENTATION FOR SENTINEL-5P AND GROUND SENSOR OBSERVATIONS

2021 ◽  
Vol XLVI-4/W2-2021 ◽  
pp. 31-36
Author(s):  
J. R. Cedeno Jimenez ◽  
D. Oxoli ◽  
M. A. Brovelli
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Open Data ◽  
Data Cube ◽  
Data Sources ◽  
Quality Monitoring ◽  
Quality Analysis ◽  
Air Quality Monitoring ◽  
Access Methods ◽  
Lombardy Region

Abstract. Nowadays, the amount of open geospatial data delivered e.g. by private and public entities, such as environmental agencies, enables outstanding possibilities to any user interested in investigating real-world phenomena. However, the availability of such information presents several challenges in terms of its practical use. These are mainly connected to the heterogeneity of data sources, formats and processing tools which have to be mastered by the user to obtain the desired results. As a relevant example, air quality monitoring requires the integration of multiple data with different spatial and temporal granularities that are often distributed by more than one provider using not uniform formats and access methods. Besides traditional air pollution ground sensors observations, novel data sources have emerged. Among them, the Sentinel-5P mission of the European Copernicus Programme is one of the most recent Earth Observation platforms providing estimates of air pollutants with daily global coverage. These estimates are promising to foster air quality analysis and monitoring by complementing ground sensors observations. Therefore, the development of data handling and analysis strategies – allowing users for a smooth integration of satellite and ground sensor observations – is key to support future air quality studies. To that end, the present work investigates the use of the Open Data Cube as a single data endpoint to incorporate ground sensors and satellite observations into local air pollution analyses. A preliminary implementation is presented using the Lombardy region (Northern Italy) as a case study.

scholarly journals ASSESSMENT OF SENTINEL-5P PERFORMANCE FOR GROUND-LEVEL AIR QUALITY MONITORING: PREPARATORY EXPERIMENTS OVER THE COVID-19 LOCKDOWN PERIOD

2020 ◽  
Vol XLIV-3/W1-2020 ◽  
pp. 111-116
Author(s):  
D. Oxoli ◽  
J. R. Cedeno Jimenez ◽  
M. A. Brovelli
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Nitrogen Dioxide ◽  
Prediction Models ◽  
Ambient Air ◽  
Data Availability ◽  
Quality Monitoring ◽  
Strong Positive Correlation ◽  
Air Quality Monitoring ◽  
Lombardy Region

Abstract. Scientific evidence has demonstrated that deterioration of ambient air quality has increased the number of deaths worldwide by appointing air pollution among the most pressing sustainability concerns. In this context, the continuous monitoring of air quality and the modelling of complex air pollution patterns is critical to protect population and ecosystems health. Availability of air quality observations has terrifically improved in the last decades allowing – nowadays – for extensive spatial and temporal resolved analysis at both global and local scale. Satellite remote sensing is mostly accountable for this data availability and is promising to foster air quality monitoring in support of traditional ground sensors measurements. In view of the above, this study compares observations from the Sentinel-5P mission of the European Copernicus Programme (the most recent Earth Observation platform providing open measurements of atmospheric constituents) with traditional ground measurements to investigate their space and time correlations across the Lombardy region (Northern Italy). The correlation analysis focused on nitrogen dioxide. The use of data collected during the COVID-19 pandemic allowed for a parallel exploration of the lockdown effects on nitrogen dioxide emissions. Results show a marked decrease in nitrogen dioxide concentrations during the lockdown and an overall strong positive correlation between satellite and ground sensors observations. These experiments are preparatory for future activities that will focus on the development of satellite-based air quality local prediction models, aiming at improving the granularity of the ground-based information available for air quality monitoring and exposure modelling.

scholarly journals Domain adaptation based deep calibration of low-cost PM2.5 sensors

2021 ◽  
Author(s):  
Sonu Kumar Jha ◽  
Mohit Kumar ◽  
Vipul Arora ◽  
Sachchida Nand Tripathi ◽  
Vidyanand Motiram Motghare ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Domain Adaptation ◽  
Low Cost ◽  
Monitoring Network ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Proposed Model ◽  
Temperature And Humidity ◽  
Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

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

2021 ◽  
pp. 94-106
Author(s):  
Porush Kumar ◽  
Kuldeep ◽  
Nilima Gautam
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Pollution Control ◽  
Urban Air Pollution ◽  
Ambient Air ◽  
Quality Monitoring ◽  
Urban Air ◽  
Air Quality Monitoring ◽  
Ambient Air Quality ◽  
Control Board

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.

scholarly journals Healthier breath for all and everywhere – Urban air quality monitoring experience

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
J Gajic ◽  
D Dimovski ◽  
B Vukajlovic ◽  
M Jevtic
Keyword(s):  
Public Health ◽  
Air Pollution ◽  
Air Quality ◽  
Health Risk ◽  
Urban Health ◽  
Urban Areas ◽  
Danube River ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Residential Areas

Abstract Issue/problem Increasing attention is being paid to air pollution as one of the greatest threats to public and urban health. The WHO’s Urban Health Initiative points out the importance of collecting data and mapping the present state of air quality in urban areas. For citizens, such engagement is enabled by the appearance of personal air quality measurement devices that use crowd-sourcing to make measurement results publicly accessible in real time. Description of the problem As a way of contributing to air pollution monitoring in their town, three PhD Public health students conducted over 40 measurements between the start of June and end of August 2018 on various locations in the city of Novi Sad, Serbia. Measurements were performed using AirBeam personal air quality monitoring devices and their results presented as μg/m3 of Particulate Matter 2.5 (PM2.5) and automatically uploaded to the internet using the Air-casting app. Results Measurements conducted in public transportation vehicles returned the rather high average value of 40 μg/m3, where coffee shops and restaurants scored an even higher value of 48,67 μg/m3. The lowest average air pollution levels were registered near the Danube river bank (5.67) and in the parks (6), while the sites near crossroads or in the street showed average air pollution of 8.33 μg/m3. Residential areas where smoking is present during the day reported 2.5 times higher PM2.5 values than those without smokers (33.8 and 12.78 μg/m3). Lessons Bearing in mind that the air quality is considered as a serious health risk in urban areas, results of this pilot investigation suggest potential health risk for citizens living in urban areas. The negative effects of combustion and smoking on air quality are strongly highlighted, as well as the positive impact of green areas and parks near residential areas. Key messages Air pollution exposure as a serious health risk in urban areas. Crowdsourcing as a way of air quality monitoring has great potential for contributing to public health.

scholarly journals One-Year Simulation of Ozone and Particulate Matter in China Using WRF/CMAQ Modeling System

2016 ◽  
Author(s):  
Jianlin Hu ◽  
Jianjun Chen ◽  
Qi Ying ◽  
Hongliang Zhang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Model Performance ◽  
Monitoring Network ◽  
Organic Aerosol ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Low Concentration ◽  
Temporal And Spatial

Abstract. China has been experiencing severe air pollution in recent decades. Although ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research &amp; Forecasting model (WRF) and the Community Multi-scale Air Quality model (CMAQ) was conducted to provide detailed temporal and spatial information of ozone (O3), PM2.5 total and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, over-prediction of O3 generally occurs at low concentration range while under-prediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in Southern China than in Northern, Central and Sichuan basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of CMAQ model in reproducing severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

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

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

&lt;p&gt;&lt;strong&gt;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.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A3B03036152).&lt;/strong&gt;&lt;/p&gt;

scholarly journals Development of an Air Quality Learning and Demonstration Center at the Arboretum at Penn State

HortScience ◽  
2004 ◽  
Vol 39 (4) ◽  
pp. 810B-810
Author(s):  
John M. Skelly ◽  
Don D. Davis ◽  
Dennis R. Decoteau*
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Pollution Abatement ◽  
Environmental Parameters ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Monitoring Methods ◽  
Penn State ◽  
Internet Connections ◽  
Quality Learning

An Air Quality Learning and Demonstration Center has been developed within the Arboretum at Penn State Univ.. The Center provides opportunities where students (of all ages) and teachers (grade-school through to classes within the Univ.) can learn about air quality as one of our most important natural resources. A seasonally interactive display of air quality monitoring instrumentation, self guided walkways through gardens of air pollution sensitive plant species, innovative techniques for demonstrating the effects of air pollutants on plants, displays of recent research findings, industry supported displays of pollution abatement technologies, and a teaching pavilion are within the Center. A Pennsylvania Dept. of Environmental Protection air quality monitoring station with ozone, sulfur dioxide, nitrogen oxides, carbon dioxide, PM < 2.5 u mass and speciation samplers, and a complete meteorological station provide data on the immediate environmental parameters. These data are relayed to an LCD crystal display board that has been mounted on the outside of the monitoring building; visitors are able to see the various measures of the air quality on a real time basis. Pannier type fiberglass display panels provide understandings of the various facets of air pollution formation and transport phenomena, air quality monitoring methods, the functions of open-top chambers, foliar symptoms expressed by pollution sensitive plants within the bioindicator gardens, and the impacts of pollution on agricultural and forested ecosystems. Handicapped accessible walkways lead visitors throughout the Center to the Teaching Pavilion that easily accommodates 80 persons. The pavilion is equipped with drop down curtains, electric power, and internet connections.

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