scholarly journals Assessing the Impact of Vehicle Speed Limits and Fleet Composition on Air Quality Near a School

2019 ◽  
Vol 16 (1) ◽  
pp. 149 ◽  
Author(s):  
Jiayi Tang ◽  
Aonghus McNabola ◽  
Bruce Misstear ◽  
Francesco Pilla ◽  
Md Saniul Alam
Keyword(s):  
Air Quality ◽  
Urban Air Pollution ◽  
Vehicle Speed ◽  
Traffic Emission ◽  
Speed Limits ◽  
School Based ◽  
Pollutant Concentrations ◽  
Fleet Composition ◽  
The Impact ◽  
Background Air

Traffic is a major source of urban air pollution that affects health, especially among children. As lower speed limits are commonly applied near schools in many cities, and different governments have different policies on vehicle fleet composition, this research estimated how different speed limits and fleet emissions affect air quality near a primary school. Based on data of traffic, weather, and background air quality records in Dublin from 2013, traffic, emission, and dispersion models were developed to assess the impact of different speed limits and fleet composition changes against current conditions. Outside the school, hypothetical speed limit changes from 30 km/h to 50 km/h could reduce the concentration of NO2 and PM10 by 3% and 2%; shifts in the fleet from diesel to petrol vehicles could reduce these pollutants by 4% and 3% but would increase the traffic-induced concentrations of CO and Benzene by 63% and 35%. These changes had significantly larger impacts on air quality on streets with higher pollutant concentrations. Findings suggest that both road safety and air quality should be considered when determining speed limits. Furthermore, fleet composition has different impacts on different pollutants and there are no clear benefits associated with incentivising either diesel or petrol engine vehicles.

scholarly journals Positive effects of COVID-19 lockdown on air quality of industrial cities (Ankleshwar and Vapi) of Western India

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ritwik Nigam ◽  
Kanvi Pandya ◽  
Alvarinho J. Luis ◽  
Raja Sengupta ◽  
Mahender Kotha
Keyword(s):  
Air Quality ◽  
Meta Analysis ◽  
Linear Regression Analysis ◽  
Quality Data ◽  
Western India ◽  
Rapid Reduction ◽  
Positive Effects ◽  
Pollutant Concentrations ◽  
The Impact

AbstractOn January 30, 2020, India recorded its first COVID-19 positive case in Kerala, which was followed by a nationwide lockdown extended in four different phases from 25th March to 31st May, 2020, and an unlock period thereafter. The lockdown has led to colossal economic loss to India; however, it has come as a respite to the environment. Utilizing the air quality index (AQI) data recorded during this adverse time, the present study is undertaken to assess the impact of lockdown on the air quality of Ankleshwar and Vapi, Gujarat, India. The AQI data obtained from the Central Pollution Control Board was assessed for four lockdown phases. We compared air quality data for the unlock phase with a coinciding period in 2019 to determine the changes in pollutant concentrations during the lockdown, analyzing daily AQI data for six pollutants (PM10, PM2.5, CO, NO2, O3, and SO2). A meta-analysis of continuous data was performed to determine the mean and standard deviation of each lockdown phase, and their differences were computed in percentage in comparison to 2019; along with the linear correlation analysis and linear regression analysis to determine the relationship among the air pollutants and their trend for the lockdown days. The results revealed different patterns of gradual to a rapid reduction in most of the pollutant concentrations (PM10, PM2.5, CO, SO2), and an increment in ozone concentration was observed due to a drastic reduction in NO2 by 80.18%. Later, increases in other pollutants were also observed as the restrictions were eased during phase-4 and unlock 1. The comparison between the two cities found that factors like distance from the Arabian coast and different industrial setups played a vital role in different emission trends.

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>

scholarly journals Oil and gas impacts on air quality in federal lands in the Bakken region: an overview of the Bakken Air Quality Study and first results

2015 ◽  
Vol 15 (20) ◽  
pp. 28749-28792 ◽  
Author(s):  
A. J. Prenni ◽  
D. E. Day ◽  
A. R. Evanoski-Cole ◽  
B. C. Sive ◽  
A. Hecobian ◽  
...  
Keyword(s):  
Air Quality ◽  
Power Plants ◽  
Oil And Gas ◽  
Quality Data ◽  
Gas Emissions ◽  
Horizontal Drilling ◽  
Federal Lands ◽  
Pollutant Concentrations ◽  
Quality Study ◽  
The Impact

Abstract. The Bakken formation contains billions of barrels of oil and gas trapped in rock and shale. Horizontal drilling and hydraulic fracturing methods have allowed for extraction of these resources, leading to exponential growth of oil production in the region over the past decade. Along with this development has come an increase in associated emissions to the atmosphere. Concern about potential impacts of these emissions on federal lands in the region prompted the National Park Service to sponsor the Bakken Air Quality Study over two winters in 2013–2014. Here we provide an overview of the study and present some initial results aimed at better understanding the impact of local oil and gas emissions on regional air quality. Data from the study, along with long term monitoring data, suggest that while power plants are still an important emissions source in the region, emissions from oil and gas activities are impacting ambient concentrations of nitrogen oxides and black carbon and may dominate recent observed trends in pollutant concentrations at some of the study sites. Measurements of volatile organic compounds also definitively show that oil and gas emissions were present in almost every air mass sampled over a period of more than four months.

scholarly journals Megacity and local contributions to regional air pollution: An aircraft case study over London

2019 ◽  
Author(s):  
Kirsti Ashworth ◽  
Silvia Bucci ◽  
Peter J. Gallimore ◽  
Junghwa Lee ◽  
Beth S. Nelson ◽  
...  
Keyword(s):  
Air Quality ◽  
Early Career ◽  
Atmospheric Measurements ◽  
Airborne Measurements ◽  
Sources Of Pollution ◽  
Pollutant Concentrations ◽  
Local Sources ◽  
The Uk ◽  
The Impact ◽  
Pollution Events

Abstract. In July 2017 three research flights circumnavigating the megacity of London were conducted as a part of the STANCO training school for students and early career researchers organised by EUFAR (European Facility for Aircraft Research). Measurements were made from the UK’s Facility for Airborne Atmospheric Measurements (FAAM) BAe-146-301 Atmospheric Research Aircraft with the aim to sample, characterise and quantify the impact of megacity outflow pollution on air quality in the surrounding region. Conditions were extremely favourable for airborne measurements and all three flights were able to observe clear pollution events along the flight path. A small change in wind direction provided sufficiently different airmass origins over the two days such that a distinct pollution plume from London, attributable marine emissions and a double-peaked dispersed area of pollution resulting from a combination of local and transported emissions were measured. We were able to analyse the effect of London emissions on air quality in the wider region and the extent to which local sources contribute to pollution events. The background air upwind of London was relatively clean during both days; concentrations of CO were 88–95 ppbv, total (measured) volatile organic compounds (VOCs) were 1.6–1.8 ppbv, and NOx were 0.7–0.8 ppbv. Downwind of London, we encountered elevations in all species with CO > 100 ppbv, VOCs 2.8–3.8 ppbv, CH4 > 2080 ppbv and NOx > 4 ppbv, and peak concentrations in individual pollution events higher still. Levels of O3 were inversely correlated with NOx during the first flight, with O3 concentrations of 37 ppbv upwind falling to ~ 26 ppbv in the well-defined London plume. Mass balance techniques were applied to estimate pollutant fluxes from London. Our calculated CO2 fluxes are within 10 % of those estimated previously, but there was a greater disparity in our estimates of CH4 and CO. On the second day, winds were lighter and downwind O3 concentrations were elevated to ~ 39–43 ppbv (from ~ 32–35 ppbv upwind), reflecting the contribution of more aged pollution to the regional background. Elevations in pollutant concentrations were dispersed over a wider area than the first day, although we also encountered a number of clear spikes from local sources. This series of flights demonstrated that megacity outflow, local fresh emissions and more distant UK sources of pollution all contribute to pollution events in the southeast of the UK. These sources must therefore all be well-characterised and constrained to understand air quality around London.

Downscaling EDGAR emissions to local emission sectors for Switzerland

2021 ◽  
Author(s):  
Jacinta Edebeli ◽  
Curdin Spirig ◽  
Julien Anet
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Measurement Data ◽  
Pilot Project ◽  
Air Quality Model ◽  
Quality Model ◽  
Emission Data ◽  
Traffic Pattern ◽  
Pollutant Concentrations ◽  
The Impact

<p>The fifth version of the Emission Database for Global Atmospheric Research (EDGAR 5.0) provides an impressive inventory of various pollutants. Pollutants from different emission sectors are available with daily, monthly and yearly temporal profiles at a high global resolution of 0.1°×0.1°. Although this resolution has been sufficient for regional air quality studies, the emissions appeared to be too coarse for local air quality studies in areas with complex topography. With Switzerland as a case study, we present our approach for downscaling EDGAR emission data to a much finer resolution of 0.02°×0.02° with the aim of modelling local air quality.</p><p>We downscaled the EDGAR emissions using a combination of GIS tools including QGIS, ArcGIS, and a series of python scripts. We obtained the surface coverage of different land use features within the defined EDGAR emission sectors from Open Street Map (OSM) using the <em>QuickOSM</em> tool in QGIS. With the calculated local surface area coverage of the emissions sectors, we downscaled the EDGAR inventory data within ArcGIS using a set of developed Arcpy script tools.</p><p>The outcome was a much finer resolved emission dataset which we fed into the WRF-CHEM air quality model within a pilot project. A comparison of the modelled pollutant concentrations using the two datasets (original EDGAR data and the downscaled data) shows an improved agreement between the downscaled dataset and the measurement data.</p><p>Studies investigating the impact of urbanization, land use change or traffic pattern on air quality may benefit from our downscaling solution, which, thanks to the global coverage of OSM, can be globally applied.</p>

scholarly journals Impact of urban parameterization on high resolution air quality forecast with the GEM – AQ model

2012 ◽  
Vol 12 (21) ◽  
pp. 10387-10404 ◽  
Author(s):  
J. Struzewska ◽  
J. W. Kaminski
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Wind Speed ◽  
Quality Parameters ◽  
Meteorological Conditions ◽  
Specific Humidity ◽  
Air Quality Forecast ◽  
Pollutant Concentrations ◽  
The Impact ◽  
Quality Forecast

Abstract. The aim of this study is to assess the impact of urban cover on high-resolution air quality forecast simulations with the GEM-AQ (Global Environmental Multiscale and Air Quality) model. The impact of urban area on the ambient atmosphere is non-stationary, and short-term variability of meteorological conditions may result in significant changes of the observed intensity of urban heat island and pollutant concentrations. In this study we used the Town Energy Balance (TEB) parameterization to represent urban effects on modelled meteorological and air quality parameters at the final nesting level with horizontal resolution of ~5 km over Southern Poland. Three one-day cases representing different meteorological conditions were selected and the model was run with and without the TEB parameterization. Three urban cover categories were used in the TEB parameterization: mid-high buildings, very low buildings and low density suburbs. Urban cover layers were constructed based on an area fraction of towns in a grid cell. To analyze the impact of urban parameterization on modelled meteorological and air quality parameters, anomalies in the lowest model layer for the air temperature, wind speed and pollutant concentrations were calculated. Anomalies of the specific humidity fields indicate that the use of the TEB parameterization leads to a systematic reduction of moisture content in the air. Comparison with temperature and wind speed measurements taken at urban background monitoring stations shows that application of urban parameterization improves model results. For primary pollutants the impact of urban areas is most significant in regions characterized with high emissions. In most cases the anomalies of NO2 and CO concentrations were negative. This reduction is most likely caused by an enhanced vertical mixing due to elevated surface temperature and modified vertical stability.

scholarly journals Road Safety Impact of Increased Rural Highway Speed Limits in British Columbia, Canada

2018 ◽  
Vol 10 (10) ◽  
pp. 3555 ◽  
Author(s):  
Jeffrey Brubacher ◽  
Herbert Chan ◽  
Shannon Erdelyi ◽  
Gordon Lovegrove ◽  
Farhad Faghihi
Keyword(s):  
British Columbia ◽  
Road Safety ◽  
Systems Approach ◽  
Vehicle Speed ◽  
Linear Regression Models ◽  
Insurance Claims ◽  
Speed Limits ◽  
Fatal Crashes ◽  
The Impact ◽  
Road Segments

Control of vehicle speed is a central tenet of the safe systems approach to road safety. Most research shows that raising speed limits results in more injuries. Advocates of higher speed limits argue that this conclusion is based on older research, that traffic fatalities are decreasing despite higher speed limits, and that modern vehicles are able to safely travel at higher speeds. These arguments were used to justify raising speed limits on rural highways in British Columbia, Canada (July 2014). We used an interrupted time series approach to evaluate the impact of these speed limit increases on fatal crashes, auto-insurance claims, and ambulance dispatches for road trauma. Events were mapped to affected road segments (with increased speed limits) and to nearby road segments (within 5 km of an affected segment). Separate linear regression models were fitted for each outcome and road segment group. Models included gasoline sales to account for changes in vehicle travel. Our main findings were significant increases in (i) total insurance claims (43.0%; 95% Confidence Interval [CI] = 16.0–76.4%), (ii) injury claims (30.0%; 95% CI = 9.5–54.2%), and (iii) fatal crashes (118.0; 95% CI = 10.9–225.1%) on affected road segments. Nearby segments had a 25.7% increase in insurance claims (95% CI = 16.1–36.1%).

scholarly journals The Effect of Nonlocal Vehicle Restriction Policy on Air Quality in Shanghai

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 299 ◽  
Author(s):  
Junjie Li ◽  
Xiao-Bing Li ◽  
Bai Li ◽  
Zhong-Ren Peng
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Traffic Management ◽  
Health And Safety ◽  
Ground Level ◽  
License Plate ◽  
Traffic Emission ◽  
Restriction Policy ◽  
Co Concentration ◽  
The Impact

In recent years, road space rationing policies have been increasingly applied as a traffic management solution to tackle congestion and traffic emission problems in big cities. Existing studies on the effect of traffic policy on air quality have mainly focused on the odd–even day traffic restriction policy or one-day-per-week restriction policy. There are few studies paying attention to the effect of nonlocal license plate restrictions on air quality in Shanghai. Restrictions toward nonlocal vehicles usually prohibit vehicles with nonlocal license plates from entering certain urban areas or using certain subsets of the road network (e.g., the elevated expressway) during specific time periods on workdays. To investigate the impact of such a policy on the residents’ exposure to pollutants, CO concentration and Air Quality Index (AQI) were compared during January and February in 2015, 2016 and 2017. Regression discontinuity (RD) was used to test the validity of nonlocal vehicle restriction on mitigating environmental pollution. Several conclusions can be made: (1) CO concentration was higher on ground-level roads on the restriction days than those in the nonrestriction days; (2) the extension of the restriction period exposed the commuters to high pollution for a longer time on the ground, which will do harm to them; and (3) the nonlocal vehicle restriction policy did play a role in improving the air quality in Shanghai when extending the evening rush period. Additionally, some suggestions are mentioned in order to improve air quality and passenger health and safety.

Estimating Vehicular Emission Impact of Nighttime Construction with VISSIM and Different MOVES Emission Estimation Approaches

2018 ◽  
Vol 2672 (25) ◽  
pp. 174-186 ◽  
Author(s):  
Chaoyi Gu ◽  
Reza Farzaneh ◽  
Geza Pesti ◽  
Gabriel Valdez ◽  
Andrew Birt
Keyword(s):  
Air Quality ◽  
Vehicle Emissions ◽  
Operating Mode ◽  
Emission Model ◽  
Test Bed ◽  
Work Zones ◽  
Traffic Emission ◽  
Emission Estimation ◽  
The Impact ◽  
Nighttime Construction

Shifting work zones from daytime to nighttime is a potential solution to air quality issues on roadway with high traffic volume and where it is undesirable to close lanes during peak hours. The expected benefit of such shifting is to reduce total fuel consumption and on-road vehicle emissions. However, the magnitude of emission reductions and air quality impacts has not been examined comprehensively at work zones. The study presented in this paper investigated the traffic-related emission impacts of work zones using an urban freeway case study. A VISSIM test bed combined with the Environmental Protection Agency’s MOVES emission model was used to estimate total emissions assuming daytime and nighttime lane-closure scenarios. Vehicle emissions were estimated using a link-based method and operating mode-based method. The results from both methods demonstrated that nighttime construction has a significant impact on both traffic speeds and vehicle emissions, primarily as a result of reductions in vehicle miles traveled. In addition, a horizontal comparison between the results from the two methods was made to assess the impact of different emission estimation approaches. The outcomes from the comparison highlight the potential importance of the operating mode-based approach for accurately estimate total traffic emission quantities when data or simulations are available.

scholarly journals Assessing vehicle fuel efficiency using a dense network of CO<sub>2</sub> observations

2021 ◽  
Author(s):  
Helen Fitzmaurice ◽  
Alexander J. Turner ◽  
Jinsol Kim ◽  
Katherine Chan ◽  
Erin R. Delaria ◽  
...  
Keyword(s):  
Air Quality ◽  
Urban Areas ◽  
Local Governments ◽  
Fuel Efficiency ◽  
Vehicle Speed ◽  
Spatial And Temporal Scales ◽  
Fleet Composition ◽  
Transportation Emissions ◽  
Neighborhood Scale ◽  
Vehicle Fuel Efficiency

Abstract. Transportation represents the largest sector of anthropogenic CO2 emissions in urban areas. Timely reductions in urban transportation emissions are critical to reaching climate goals set by international treaties, national policies, and local governments. Transportation emissions also remain one of the largest contributors to both poor air quality (AQ) and to inequities in AQ exposure. As municipal and regional governments create policy targeted at reducing transportation emissions, the ability to evaluate the efficacy of such emission reduction strategies at the spatial and temporal scales of neighborhoods is increasingly important. However, the current state of the art in emissions monitoring does not provide the temporal, sectoral, or spatial resolution necessary to track changes in emissions and provide feedback on the efficacy of such policies at a neighborhood scale. The BErkeley Air Quality and CO2 Network (BEACO2N) has previously been shown to provide constraints on emissions from the vehicle sector in aggregate over a ~1300 km2 multi-city spatial domain. Here, we focus on a 5 km, high volume, stretch of highway in the SF Bay area. We show that inversion of the BEACO2N measurements can be used to understand two factors that affect fuel efficiency: vehicle speed and fleet composition. The CO2 emission rate of the average vehicle (g/vkm) are shown to vary by as much as 27 % at different times of a typical weekday because of changes in vehicle speed and fleet composition. The BEACO2N-derived emissions estimates are consistent to within ~3 % of estimates derived from publicly available measures of vehicle type, number, and speed, providing direct observational support for the accuracy of the Emissions FACtor model (EMFAC) of vehicle fuel efficiency.

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