scholarly journals A Survey on Different Machine Learning Techniques for Air Quality Forecasting for Urban Air Pollution

2019 ◽  
Vol 7 (4) ◽  
pp. 2185-2194
Author(s):  
Sayali Nemade
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Urban Air Pollution ◽  
Machine Learning Techniques ◽  
Urban Air ◽  
Learning Techniques ◽  
Air Quality Forecasting

From Urban Air Quality Forecasting and Information Systems to Integrated Urban Hydrometeorology, Climate and Environment Systems and Services for Smart Cities

2020 ◽  
Author(s):  
Alexander Baklanov ◽  
Keyword(s):  
Climate Change ◽  
Air Pollution ◽  
Air Quality ◽  
Urban Areas ◽  
Urban Air Pollution ◽  
Urban Air Quality ◽  
Urban Air ◽  
Climate Services ◽  
Urban Weather ◽  
Air Quality Forecasting

<p>This presentation is analysing a modern evolution in research and development from specific urban air quality systems to multi-hazard and integrated urban weather, environment and climate systems and services and provides an overview of joint results of large EU FP FUMAPEX, MEGAPOLI, EuMetChem and MarcoPolo projects and international WMO GURME and IUS teams. </p><p>Urban air pollution is still one of the key environmental issues for many cities around the world. A number of recent and previous international studies have been initiated to explore these issues. In particular relevant experience from several European projects will be demonstrated. MEGAPOLI studies aimed to assess the impacts of megacities and large air-pollution hotspots on local, regional and global air quality; to quantify feedback mechanisms linking megacity air quality, local and regional climates, and global climate change; and to develop improved tools for predicting air pollution levels in megacities (doi:10.5194/asr-4-115-2010). FUMAPEX developed for the first time an integrated system encompassing emissions, urban meteorology and population exposure for urban air pollution episode forecasting, the assessment of urban air quality and health effects, and for emergency preparedness issues for urban areas (UAQIFS: Urban Air Quality Forecasting and Information System; doi.org/10.5194/acp-6-2005-2006; doi.org/10.5194/acp-7-855-2007).</p><p>While important advances have been made, new interdisciplinary research studies are needed to increase our understanding of the interactions between emissions, air quality, and regional and global climates. Studies need to address both basic and applied research and bridge the spatial and temporal scales connecting local emissions, air quality and weather with climate and global atmospheric chemistry. WMO has established the Global Atmosphere Watch (GAW) Urban Research Meteorology and Environment (GURME) project which provides an important research contribution to the integrated urban services.</p><p>Most of the disasters affecting urban areas are of a hydro-meteorological nature and these have increased due to climate change. Cities are also responsible not only for air pollution emissions, but also for generating up to 70% of GHG emissions that drive large scale climate change. Thus, there is a strong feedback between contributions of cities to environmental health, climate change and the impacts of climate change on cities and these phases of the problem should not be considered separately. There is a critical need to consider the problem in a complex manner with interactions of climate change and disaster risk reduction for urban areas (doi:10.1016/j.atmosenv.2015.11.059, doi.org/10.1016/j.uclim.2017.05.004).</p><p>WMO is promoting safe, healthy and resilient cities through the development of Integrated Urban Weather, Environment and Climate Services (IUS). The aim is to build urban services that meet the special needs of cities through a combination of dense observation networks, high-resolution forecasts, multi-hazard early warning systems, disaster management plans and climate services. This approach gives cities the tools they need to reduce emissions, build thriving and resilient communities and implement the UN Sustainable Development Goals. The Guidance on IUS, developed by a WMO inter-programme working group, documents and shares the good practices that will allow countries and cities to improve the resilience of urban areas to a great variety of natural and other hazards (https://library.wmo.int/doc_num.php?explnum_id=9903).</p>

Urban Air Pollution Mapping Using Fleet Vehicles as Mobile Monitors and Machine Learning

2021 ◽  
Vol 55 (8) ◽  
pp. 5579-5588
Author(s):  
Bu Zhao ◽  
Long Yu ◽  
Chunyan Wang ◽  
Chenyang Shuai ◽  
Ji Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Urban Air Pollution ◽  
Urban Air

scholarly journals Near real-time air quality forecasts using the NASA GEOS model

2021 ◽  
Author(s):  
K. Emma Knowland ◽  
Christoph Keller ◽  
Krzysztof Wargan ◽  
Brad Weir ◽  
Pamela Wales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Real Time ◽  
Weather Forecasting ◽  
Atmospheric Composition ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Wide Range ◽  
Reactive Trace Gases ◽  
Assimilation System

<p>NASA's Global Modeling and Assimilation Office (GMAO) produces high-resolution global forecasts for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5). Satellite observations are assimilated into the system for improved representation of weather and smoke. The assimilation system is being expanded to include chemically reactive trace gases. We discuss current capabilities of the GEOS Constituent Data Assimilation System (CoDAS) to improve atmospheric composition modeling and possible future directions, notably incorporating new observations (TROPOMI, geostationary satellites) and machine learning techniques. We show how machine learning techniques can be used to correct for sub-grid-scale variability, which further improves model estimates at a given observation site.</p>

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 Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques

2020 ◽  
pp. 1-8
Author(s):  
Jasleen Kaur Sethi ◽  
Mamta Mittal
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Air Pollutants ◽  
Machine Learning Techniques ◽  
Environmental Restoration ◽  
The Novel ◽  
Ozone Pollution ◽  
Learning Techniques ◽  
Novel Coronavirus ◽  
The Impact

ABSTRACT Objective: The focus of this study is to monitor the effect of lockdown on the various air pollutants due to the coronavirus disease (COVID-19) pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced. Methods: Various machine learning techniques: Decision Trees, Linear Regression, and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during the lockdown period and last two years, 2018 and 2019, has been presented. Results: From the experimental work, it has been observed that the pollutants ozone and toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are ozone, NH3, NO2, and PM10. Conclusions: The novel coronavirus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control ozone pollution, as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

scholarly journals Comparative Analysis of Machine Learning Techniques for Predicting Air Quality in Smart Cities

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 128325-128338 ◽  
Author(s):  
Saba Ameer ◽  
Munam Ali Shah ◽  
Abid Khan ◽  
Houbing Song ◽  
Carsten Maple ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Air Quality ◽  
Smart Cities ◽  
Machine Learning Techniques ◽  
Learning Techniques

ACROSS: An Observational Campaign to Improve Understanding of Photochemistry of Mixed Urban and Biogenic Air Masses

2020 ◽  
Author(s):  
Christopher Cantrell ◽  
Vincent Michoud ◽  
Paola Formenti ◽  
Jean-Francois Doussin ◽  
Aline Gratien ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Field Study ◽  
Organic Compounds ◽  
Numerical Models ◽  
Urban Air Pollution ◽  
Central Component ◽  
Urban Air ◽  
Oxides Of Nitrogen ◽  
Chemical Structures

<p>In recent decades, significant progress has been made in understanding the causes and impacts of urban air pollution, generally leading to improved air quality through enhanced knowledge and regulatory action. While a significant number of people still die prematurely each year from air pollution, progress continues to be made. Scientific investigation has exposed the processes by which primary pollutants, such as oxides of nitrogen and volatile organic compounds, are processed in the atmosphere, leading to their oxidation and ultimate removal, while at the same time producing secondary species such as ozone and organic aerosols.</p><p>Research has uncovered the complex chemistry of natural organic compounds released from trees and other plants. Because of the chemical structures of these compounds, they react somewhat differently than organic substances typically found in urban environments. The ACROSS (<strong>A</strong>tmospheric <strong>C</strong>hemist<strong>R</strong>y <strong>O</strong>f the <strong>S</strong>uburban fore<strong>S</strong>t) project focuses on scientific research to understand the detailed chemistry and physics of urban air mixed with biogenic emissions with the goals to increase detailed understanding of the chemical processes and to use this knowledge to improve the performance of air quality models. Enhanced knowledge and improved models will allow society to develop better strategies to improve air quality and save lives.</p><p>The central component of ACROSS is a comprehensive summertime field study with many instruments for the measurement of primary and secondary constituents. Measurements will be made from research aircraft, a tower located in a forest, tethered balloons and/or drones, and mobile platforms. Observations from the field study will be analyzed in a variety of ways involving statistical approaches and comparisons with different types of numerical models.</p><p>This presentation describes activities in preparation of the ACROSS measurement campaign and provides information for interested parties to become involved.</p>

scholarly journals ASSESSING POPULATION SENSITIVITY TO URBAN AIR POLLUTION USING GOOGLE TRENDS AND REMOTE SENSING DATASETS

2020 ◽  
Vol XLII-3/W11 ◽  
pp. 93-100
Author(s):  
P. Misra ◽  
W. Takeuchi
Keyword(s):  
Remote Sensing ◽  
Air Pollution ◽  
Air Quality ◽  
Urban Air Pollution ◽  
Aerosol Concentration ◽  
Google Trends ◽  
Urban Air ◽  
Indirect Measure ◽  
Search Volume ◽  
Fine Aerosol

Abstract. This study demonstrates relationship between remote sensing satellite retrieved fien aerosol concentration and web-based search volumes of air quality related keywords. People’s perception of urban air pollution can verify policy effectiveness and gauge acceptability of policies. As a serious health issue in Asian cities, population may express concern or uncertainty for air pollution risk by performing search on the web to seek answers. A ‘social sensing’ approach that monitors such search queries, may assess people’ perception about air pollution as a risk. We hypothesize that trend and volume of searches show impact of air pollution on general population. The objectives of this research are to identify those atmospheric conditions under which relative search volume (RSV) obtained from Google Trends shows correlation with measured fine aerosol concentration, and to compare search volume sensitivity to rise in aerosol concentration in seven Asian megacities. We considered weekly relative search volumes from Google Trends (GT) for a four year period from January, 2015 to December, 2018 representing diverse PM2.5 concentrations. Search volumes for keywords corresponding to perception of air quality (‘air pollution’) and health effects (‘cough’ and ‘asthma’) were considered. To represent PM2.5 we used fine aerosol indicator developed in an earlier research. The results suggest that tendency to search for ‘air pollution’ and ‘cough’ occurs when AirRGB R is in excess and temperature is below the baseline values. Consistent with this, in cities with high baseline concentrations, sensitivity to rise in AirRGB R is also comparatively lower. The result of this study can used as an indirect measure of awareness in the form of perception and sensitivity of population to air quality. Such an analysis could be useful for forecasting health risks specially in cities lacking dedicated services.

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