The Use of MM5-CMAQ Air Pollution Modelling System for Real-Time and Forecasted Air Quality Impact of Industrial Emissions

2005 ◽  
pp. 327-336
Author(s):  
R. San José ◽  
Juan L. Pérez ◽  
Rosa M. González
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Industrial Emissions ◽  
Air Pollution Modelling ◽  
Modelling System

scholarly journals REAL-TIME ENVIRONMENTAL SENSORS TO IMPROVE HEALTH IN THE SENSING CITY

2016 ◽  
Vol XLI-B2 ◽  
pp. 729-733 ◽  
Author(s):  
L. Marek ◽  
M. Campbell ◽  
M. Epton ◽  
M. Storer ◽  
S. Kingham
Keyword(s):  
Quality Of Life ◽  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Low Cost ◽  
Monitoring Network ◽  
Environmental Data ◽  
Chronic Obstructive ◽  
Actual Usage

The opportunity of an emerging smart city in post-disaster Christchurch has been explored as a way to improve the quality of life of people suffering Chronic Obstructive Pulmonary Disease (COPD), which is a progressive disease that affects respiratory function. It affects 1 in 15 New Zealanders and is the 4th largest cause of death, with significant costs to the health system. While, cigarette smoking is the leading cause of COPD, long-term exposure to other lung irritants, such as air pollution, chemical fumes, or dust can also cause and exacerbate it. Currently, we do know little what happens to the patients with COPD after they leave a doctor’s care. By learning more about patients’ movements in space and time, we can better understand the impacts of both the environment and personal mobility on the disease. This research is studying patients with COPD by using GPS-enabled smartphones, combined with the data about their spatiotemporal movements and information about their actual usage of medication in near real-time. We measure environmental data in the city, including air pollution, humidity and temperature and how this may subsequently be associated with COPD symptoms. In addition to the existing air quality monitoring network, to improve the spatial scale of our analysis, we deployed a series of low-cost Internet of Things (IoT) air quality sensors as well. The study demonstrates how health devices, smartphones and IoT sensors are becoming a part of a new health data ecosystem and how their usage could provide information about high-risk health hotspots, which, in the longer term, could lead to improvement in the quality of life for patients with COPD.

scholarly journals Real Time Air Quality Index from Various Locations

2020 ◽  
Vol 9 (2) ◽  
pp. 368-372
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Environmental Protection Agency ◽  
Quality Index ◽  
Ground Level ◽  
Air Quality Index ◽  
Time Data ◽  
Open Government Data ◽  
Government Data

The surveys regarding air pollution shows that there has been a hasty growth due to the emission of fuels and exhaust gases from factories. The Air Quality Index (AQI) has been launched to note the contemporary status of the air quality. The intent of AQI is to aid every individual know how the regional air quality will make an impact on them. The Environmental Protection Agency assess the AQI for five major air pollutants namely Nitrogen dioxide (NO2), ground-level ozone (O3), particle pollution (PM10, PM2.5), carbon monoxide (CO), and sulphur dioxide (SO2). The intent of the project is to congregate real-time Air Quality Index from distinct monitoring stations across India, analysing the data and reporting on it. Collect the real-time data using the API key provided by Open Government Data (OGD) platform India. This is done by making use of Microsoft Business Intelligence (MSBI) and Power BI Tools to transform, analyse and visualize the data. This project can be utilized to develop various programs like Ozone today in Europe and in mobile applications which acts as an alert system that can protect people from air pollution.

Zasavje canyon regional online air pollution modelling system in highly complex terrain - description and validation

2012 ◽  
Vol 50 (1/2/3/4) ◽  
pp. 22 ◽  
Author(s):  
Primož Mlakar ◽  
Marija Zlata Božnar ◽  
Boštjan Grašič ◽  
Gianni Tinarelli
Keyword(s):  
Air Pollution ◽  
Complex Terrain ◽  
Air Pollution Modelling ◽  
Modelling System

scholarly journals A tale of four cities: A semantic analysis comparing the newspaper coverage of air pollution in Hong Kong, London, Pittsburgh, and Tianjin from 2014 to 2017

2019 ◽  
Vol 41 (1) ◽  
pp. 37-52
Author(s):  
Tongxin Sun ◽  
Bu Zhong
Keyword(s):  
Air Pollution ◽  
Hong Kong ◽  
Air Quality ◽  
Real Time ◽  
Semantic Analysis ◽  
Newspaper Coverage ◽  
Word Count ◽  
The Public ◽  
Computer Aided ◽  
Linguistic Inquiry

A computer-aided semantic analysis (using Linguistic Inquiry and Word Count [LIWC]) examined how newspaper coverage of air pollution from 2014 to 2017 may affect the public agenda in four cities—Hong Kong, London, Pittsburgh, and Tianjin. Results show that after controlling for the real-time air quality, the agenda-setting effect was found in Hong Kong, London, and Pittsburgh, but not Tianjin. Tianjin’s reports also contained more future-framed words but fewer present-framed words than other cities.

From Google Maps to Air Quality: a big data approach to modelling real-time NO2 concentrations in an urban street canyon from road traffic data

2020 ◽  
Author(s):  
Helen Pearce ◽  
Zhaoya Gong ◽  
Xiaoming Cai ◽  
William Bloss
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Street Canyon ◽  
Box Model ◽  
Traffic Data ◽  
Flow Data ◽  
Pollutant Concentrations ◽  
Background Air ◽  
Model Chain

<p>In most European cities, the key air pollutants driving adverse health outcomes are nitrogen dioxide (NO2) and fine particulate matter (PM2.5), with 64% of new paediatric asthma cases in urban centres attributed to elevated NO2 levels (Achakulwisut et al., 2019). In the complex landscape of a city, a synthesis of techniques to quantify air pollution is required to account for variations in traffic, meteorology, and urban geometry.</p><p>Here, we present the results from a comparison study between measured air pollutant data collected at Marylebone Road, London and the output from a three-stage modelling chain. This site was chosen due to the availability of road-side air quality data collected within a street canyon (aspect ratio approximately equal to 1) and daily traffic flow in excess of 70,000 motor vehicles. The modelling chain consists of: 1) real-time traffic information of vehicle journey times, 2) speed-related emission calculations, and 3) air quality box-model to simulate the interaction of pollutants within the environment.</p><p>While the transport sector accounts for much of the outdoor air pollution in UK cities, a limiting factor of current techniques is that traffic is approximated at coarse temporal and spatial resolutions. In this study, we present a novel technique that helps to ‘fill in’ the gaps in our traffic data by harnessing the power of real-time queries to Google Maps to obtain travel times between fixed locations, enabling the derivation of average vehicle speeds. This dataset can then be used to determine more accurate emission factors for NOx. Total emissions are then calculated with the aid of traffic flow data and vehicle fleet characteristics. The air quality box model simulates photochemical reactions that form NO2, the exchange of pollutants with the background air aloft, and advection of pollutants along the street.</p><p>Hourly travel times and total vehicle flow data were collected between July and October 2019, totalling 905 observations and calculated emissions values. Meteorological data from Heathrow airport and background air quality from the Kensington AURN site were used as supporting inputs to the air quality box model. Each observation was treated as a starting point of the box model, and the simulation was run for 1 hour, with mixing due to advection occurring every 60 seconds. Results are promising; when using the full model chain modelled and measured NO2 concentrations are significantly correlated (r = 0.467, p < 0.000). In comparison, when a constant speed of 30 mph is used to calculate total emissions, therefore excluding the impact of congestion, the strength of the correlation decreases (r = 0.362, p < 0.000) and the model underestimates pollutant concentrations.</p><p>The applications of this model chain are vast. For any street that is covered by a suitable mapping platform and has available data on vehicle numbers, it would be possible to provide a real-time estimation of pollutant concentrations at a high temporal resolution. This could be utilised in several ways, such as: assessing policy implementation, and providing a high resolution input for air quality modelling and health exposure studies.</p>

Dealing with air pollution in Latin America: the case of Quito, Ecuador

1999 ◽  
Vol 4 (3) ◽  
pp. 375-388 ◽  
Author(s):  
JORGE JURADO ◽  
DOUGLAS SOUTHGATE
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Respiratory Illness ◽  
Medical Expenditures ◽  
Capital City ◽  
Motor Vehicles ◽  
Industrial Emissions ◽  
Manufacturing Plants ◽  
Air Quality Improvement ◽  
Primary Focus

Located in a high Andean valley, Ecuador's capital city suffers from severe air pollution, emitted by manufacturing plants as well as motor vehicles. Improving air quality would result in diminished respiratory illness, which currently costs Quito's residents several millions of dollars annually in lost earnings and medical expenditures. Technology transfer has succeeded in reducing industrial emissions at a modest cost. But diesel-fueled trucks and buses, which are a major source of various pollutants, have been the primary focus of the local government's strategy for air quality improvement. To date, that strategy has met with some success, although future initiatives will involve higher abatement expenses and therefore will test the commitment of municipal authorities and the citizens they represent to pollution control.

scholarly journals Comparisons of aerosol optical depth provided by seviri satellite observations and CAMx air quality modelling

2015 ◽  
Vol XL-7/W3 ◽  
pp. 187-193 ◽  
Author(s):  
A. Fernandes ◽  
M. Riffler ◽  
J. Ferreira ◽  
S. Wunderle ◽  
C. Borrego ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Satellite Data ◽  
Spatial Variations ◽  
Transport Processes ◽  
Satellite Observations ◽  
High Temporal Resolution ◽  
Vertical Column ◽  
Air Pollution Modelling ◽  
Spatial Coverage

Satellite data provide high spatial coverage and characterization of atmospheric components for vertical column. Additionally, the use of air pollution modelling in combination with satellite data opens the challenging perspective to analyse the contribution of different pollution sources and transport processes. The main objective of this work is to study the AOD over Portugal using satellite observations in combination with air pollution modelling. For this purpose, satellite data provided by Spinning Enhanced Visible and Infra-Red Imager (SEVIRI) on-board the geostationary Meteosat-9 satellite on AOD at 550 nm and modelling results from the Chemical Transport Model (CAMx - Comprehensive Air quality Model) were analysed. The study period was May 2011 and the aim was to analyse the spatial variations of AOD over Portugal. In this study, a multi-temporal technique to retrieve AOD over land from SEVIRI was used. The proposed method takes advantage of SEVIRI's high temporal resolution of 15 minutes and high spatial resolution. <br><br> CAMx provides the size distribution of each aerosol constituent among a number of fixed size sections. For post processing, CAMx output species per size bin have been grouped into total particulate sulphate (PSO4), total primary and secondary organic aerosols (POA + SOA), total primary elemental carbon (PEC) and primary inert material per size bin (CRST_1 to CRST_4) to be used in AOD quantification. The AOD was calculated by integration of aerosol extinction coefficient (Qext) on the vertical column. The results were analysed in terms of temporal and spatial variations. The analysis points out that the implemented methodology provides a good spatial agreement between modelling results and satellite observation for dust outbreak studied (10th -17th of May 2011). A correlation coefficient of r=0.79 was found between the two datasets. This work provides relevant background to start the integration of these two different types of the data in order to improve air pollution assessment.

scholarly journals Impacts of Modifiable Factors on Ambient Air Pollution: A Case Study of COVID-19 Shutdowns

2020 ◽  
Author(s):  
Rebecca Tanzer-Gruener ◽  
Jiayu Li ◽  
s. rose eilenberg ◽  
Allen Robinson ◽  
Albert Presto
Keyword(s):  
Air Pollution ◽  
Real Time ◽  
Low Cost ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Activity Levels ◽  
Industrial Emissions ◽  
Modifiable Factors ◽  
Business As Usual ◽  
The Impact

Modifiable sources of air pollution such as traffic, cooking, and electricity generation emissions can be modulated either by changing activity levels or source intensity. Although air pollution regulations typically target reducing emission factors rather than altering activity, the COVID-19 related closures offered a novel opportunity to observe and quantify the impact of activity levels of modifiable factors on ambient air pollution in real-time. We use data from a network of twenty-seven low-cost Real-time Affordable Multi-Pollutant (RAMP) sensor packages deployed throughout urban and suburban Pittsburgh along with data from EPA regulatory monitors. The RAMP locations were divided into four site groups based on land use (High Traffic, Urban Residential, Suburban Residential, and Industrial). Concentrations of PM2.5, CO, and NO2 following the COVID-related closures at each site group were compared to measurements from “business as usual” periods in March 2019 and 2020. Overall, PM2.5 concentrations decreased across the domain by 3 μg/m3. Intra-day variabilities of the pollutants were computed to attribute pollutant enhancements to specific emission sources (i.e. traffic and industrial emissions). There was no significant change in the industrial related intra-day variability of PM2.5 at the Industrial sites following the COVID-related closures. The morning rush hour induced CO and NO2 concentrations at the High Traffic sites were reduced by 57% and 43%, respectively, which is consistent with the observed reduction in commuter traffic (~50%). The morning rush hour PM2.5 enhancement from traffic emissions fell from ~1.5 μg/m3 to ~0 μg/m3 across all site groups. This translates to a reduction of 0.125 μg/m3 in the daily average PM2.5 concentration. If PM2.5 National Ambient Air Quality Standards (NAAQS) are tightened these calculations shed light on to what extent reductions in traffic related emissions are able to aid in meeting more stringent regulations.

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