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>

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 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.

scholarly journals High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam

2019 ◽  
Author(s):  
Bas Mijling
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Low Cost ◽  
Urban Air Pollution ◽  
Heterogeneous Data ◽  
Urban Air Quality ◽  
Quality Data ◽  
Urban Air ◽  
Reference Measurements ◽  
Concentration Patterns

Abstract. In many cities around the world people are exposed to elevated levels of air pollution. Often local air quality is not well known due to the sparseness of official monitoring networks, or unrealistic assumptions being made in urban air quality models. Low-cost sensor technology, which has become available in recent years, has the potential to provide complementary information. Unfortunately, an integrated interpretation of urban air pollution based on different sources is not straightforward because of the localized nature of air pollution, and the large uncertainties associated with measurements of low-cost sensors. In this study, we present a practical approach to producing high spatio-temporal resolution maps of urban air pollution capable of assimilating air quality data from heterogeneous data streams. It offers a two-step solution: (1) building a versatile air quality model, driven by an open source atmospheric dispersion model and emission proxies from open data sources, and (2) a practical spatial interpolation scheme, capable of assimilating observations with different accuracies. The methodology, called Retina, has been applied and evaluated for nitrogen dioxide (NO2) in Amsterdam, the Netherlands, during the summer of 2016. The assimilation of reference measurements results in hourly maps with a typical accuracy of 39 % within 2 km of an observation location, and 53 % at larger distances. When low-cost measurements of the Urban AirQ campaign are included, the maps reveal more detailed concentration patterns in areas which are undersampled by the official network. During the summer holiday period, NO2 concentrations drop about 10 % due to reduced urban activity. The reduction is less in the historic city center, while strongest reductions are found around the access ways to the tunnel connecting the northern and the southern part of the city, which was closed for maintenance. The changing concentration patterns indicate how traffic flow is redirected to other main roads. Overall, we show that Retina can be applied for an enhanced understanding of reference measurements, and as a framework to integrate low-cost measurements next to reference measurements in order to get better localized information in urban areas.

scholarly journals Integrated systems for forecasting urban meteorology, air pollution and population exposure

2007 ◽  
Vol 7 (3) ◽  
pp. 855-874 ◽  
Author(s):  
A. Baklanov ◽  
O. Hänninen ◽  
L. H. Slørdal ◽  
J. Kukkonen ◽  
N. Bjergene ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Health Effects ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Urban Air Quality ◽  
Population Exposure ◽  
Quality Information ◽  
Urban Air ◽  
Initial Development

Abstract. Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement systems require accurate forecasting of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and Forecasting Systems (UAQIFS) in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the systems in three Nordic cities: Helsinki and Oslo for assessment and forecasting of urban air pollution and Copenhagen for urban emergency preparedness.

scholarly journals Integrated systems for forecasting urban meteorology, air pollution and population exposure

2006 ◽  
Vol 6 (2) ◽  
pp. 1867-1913 ◽  
Author(s):  
A. Baklanov ◽  
O. Hänninen ◽  
L. H. Slørdal ◽  
J. Kukkonen ◽  
N. Bjergene ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Health Effects ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Urban Air Quality ◽  
Population Exposure ◽  
Quality Information ◽  
Urban Air ◽  
Initial Development

Abstract. Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement systems require accurate forecasting of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and Forecasting Systems (UAQIFS) in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the systems in three Nordic cities: Helsinki and Oslo for assessment and forecasting of urban air pollution and Copenhagen for urban emergency preparedness.

scholarly journals Do Rail Transits Improve Local Air Quality? Take Chengdu-Nanchang for Example

2021 ◽  
Author(s):  
Xueyuan Wang ◽  
Yuping Wang ◽  
Zhijian Zhang ◽  
Jingwei Li
Keyword(s):  
Air Pollution ◽  
Carbon Monoxide ◽  
Air Quality ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Urban Air Pollution ◽  
Point Estimate ◽  
Urban Air ◽  
Rail Transit ◽  
Transit System

Abstract Many cities in China have invested the city’s rail transit system to reduce urban air pollution and traffic congestion. Earlier studies rarely compare the effects of rail transit on urban air quality in different cities, providing little guidance to urban planners in solving traffic congestion and air quality. By using the rail transit lines in Chengdu and Nanchang as case studies, this paper attempts to examine the effect of rail transit on air pollution. Data were collected from 18 monitoring stations distributed along the chosen rail transit lines in both cities during the period 2014 to 2016 and analyzed using the regression discontinuity design to address the potential endogenous location of subway stations. The results show that subway opening in Nanchang has a better reductions from automobile exhaust than that in Chengdu, specifically, carbon monoxide pollution, one key tailpipe pollutant, experienced a 10.23% greater reduction after Nanchang Metro Line 1 opened. On the contrary, the point estimate for carbon monoxide in Chengdu is 22.42% and statistically significant at the 1% level. Nanchang Metro Line 1 does play an important role in road traffic externalities, but the benefit was not huge enough to change the overall air quality. On the contrary, the opening of the Chengdu Metro Line 4 is unlikely to yield improvements in air quality.

Intra-Urban Air Pollution Concentrations, Perceived Air Quality, And Greenspace

2015 ◽  
Vol 2015 (1) ◽  
pp. 2887
Author(s):  
Jessie L Carr Shmool ◽  
Colleen Reid ◽  
Laura D Kubzansky ◽  
John Spengler ◽  
Jane E Clougherty
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Air Pollution ◽  
Urban Air ◽  
Pollution Concentrations

scholarly journals A Traffic-Based Method to Predict and Map Urban Air Quality

2020 ◽  
Vol 10 (6) ◽  
pp. 2035 ◽  
Author(s):  
Rasa Zalakeviciute ◽  
Marco Bastidas ◽  
Adrian Buenaño ◽  
Yves Rybarczyk
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Traffic Flow ◽  
Low Income ◽  
Urban Air Pollution ◽  
Monitoring Network ◽  
Time Of Day ◽  
Low Income Countries ◽  
Population Exposure ◽  
Urban Air

As global urbanization, industrialization, and motorization keep worsening air quality, a continuous rise in health problems is projected. Limited spatial resolution of the information on air quality inhibits full comprehension of urban population exposure. Therefore, we propose a method to predict urban air pollution from traffic by extracting data from Web-based applications (Google Traffic). We apply a machine learning approach by training a decision tree algorithm (C4.8) to predict the concentration of PM2.5 during the morning pollution peak from: (i) an interpolation (inverse distance weighting) of the value registered at the monitoring stations, (ii) traffic flow, and (iii) traffic flow + time of the day. The results show that the prediction from traffic outperforms the one provided by the monitoring network (average of 65.5% for the former vs. 57% for the latter). Adding the time of day increases the accuracy by an average of 6.5%. Considering the good accuracy on different days, the proposed method seems to be robust enough to create general models able to predict air pollution from traffic conditions. This affordable method, although beneficial for any city, is particularly relevant for low-income countries, because it offers an economically sustainable technique to address air quality issues faced by the developing world.

Sign in / Sign up

Export Citation Format

Share Document