Mobile air monitoring data processing strategies and effects on spatial air pollution trends

Abstract. The collection of real-time air quality measurements while in motion (i.e., mobile monitoring) is currently conducted worldwide to evaluate in situ emissions, local air quality trends, and air pollutant exposure. This measurement strategy pushes the limits of traditional data analysis with complex second-by-second multipollutant data varying as a function of time and location. Data reduction and filtering techniques are often applied to deduce trends, such as pollutant spatial gradients downwind of a highway. However, rarely do mobile monitoring studies report the sensitivity of their results to the chosen data processing approaches. The study being reported here utilized a large mobile monitoring dataset collected on a roadway network in central North Carolina to explore common data processing strategies including time-alignment, short-term emissions event detection, background estimation, and averaging techniques. One-second time resolution measurements of ultrafine particles ≤ 100 nm in diameter (UFPs), black carbon (BC), particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO2), and nitrogen dioxide (NO2) were collected on twelve unique driving routes that were repeatedly sampled. Analyses demonstrate that the multiple emissions event detection strategies reported produce generally similar results and that utilizing a median (as opposed to a mean) as a summary statistic may be sufficient to avoid bias in near-source spatial trends. Background levels of the pollutants are shown to vary with time, and the estimated contributions of the background to the mean pollutant concentrations were: BC (6%), PM2.5–10 (12%), UFPs (19%), CO (38%), PM10 (45%), NO2 (51%), PM2.5 (56%), and CO2 (86%). Lastly, while temporal smoothing (e.g., 5 s averages) results in weak pair-wise correlation and the blurring of spatial trends, spatial averaging (e.g., 10 m) is demonstrated to increase correlation and refine spatial trends.

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Mobile air monitoring data-processing strategies and effects on spatial air pollution trends

Atmospheric Measurement Techniques ◽

10.5194/amt-7-2169-2014 ◽

2014 ◽

Vol 7 (7) ◽

pp. 2169-2183 ◽

Cited By ~ 47

Author(s):

H. L. Brantley ◽

G. S. W. Hagler ◽

E. S. Kimbrough ◽

R. W. Williams ◽

S. Mukerjee ◽

...

Keyword(s):

Air Quality ◽

Data Processing ◽

Ultrafine Particles ◽

Air Pollutant ◽

Monitoring Data ◽

Mobile Monitoring ◽

Data Set ◽

Exhaust Plume ◽

Processing Strategies ◽

Spatial Trends

Abstract. The collection of real-time air quality measurements while in motion (i.e., mobile monitoring) is currently conducted worldwide to evaluate in situ emissions, local air quality trends, and air pollutant exposure. This measurement strategy pushes the limits of traditional data analysis with complex second-by-second multipollutant data varying as a function of time and location. Data reduction and filtering techniques are often applied to deduce trends, such as pollutant spatial gradients downwind of a highway. However, rarely do mobile monitoring studies report the sensitivity of their results to the chosen data-processing approaches. The study being reported here utilized 40 h (> 140 000 observations) of mobile monitoring data collected on a roadway network in central North Carolina to explore common data-processing strategies including local emission plume detection, background estimation, and averaging techniques for spatial trend analyses. One-second time resolution measurements of ultrafine particles (UFPs), black carbon (BC), particulate matter (PM), carbon monoxide (CO), and nitrogen dioxide (NO2) were collected on 12 unique driving routes that were each sampled repeatedly. The route with the highest number of repetitions was used to compare local exhaust plume detection and averaging methods. Analyses demonstrate that the multiple local exhaust plume detection strategies reported produce generally similar results and that utilizing a median of measurements taken within a specified route segment (as opposed to a mean) may be sufficient to avoid bias in near-source spatial trends. A time-series-based method of estimating background concentrations was shown to produce similar but slightly lower estimates than a location-based method. For the complete data set the estimated contributions of the background to the mean pollutant concentrations were as follows: BC (15%), UFPs (26%), CO (41%), PM2.5-10 (45%), NO2 (57%), PM10 (60%), PM2.5 (68%). Lastly, while temporal smoothing (e.g., 5 s averages) results in weak pair-wise correlation and the blurring of spatial trends, spatial averaging (e.g., 10 m) is demonstrated to increase correlation and refine spatial trends.

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Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: Impacts of COVID-19 pandemic lockdown

10.5194/acp-2020-1169 ◽

2020 ◽

Author(s):

Shibao Wang ◽

Yun Ma ◽

Zhongrui Wang ◽

Lei Wang ◽

Xuguang Chi ◽

...

Keyword(s):

Air Quality ◽

Air Pollutants ◽

Low Cost ◽

Air Pollutant ◽

Mitigation Strategies ◽

Traffic Emissions ◽

Urban Air ◽

Industrial Emissions ◽

Gis Technology ◽

Mobile Monitoring

Abstract. The development of low-cost sensors and novel calibration algorithms provides new hints to complement conventional ground-based observation sites to evaluate the spatial and temporal distribution of pollutants on hyper-local scales (tens of meters). Here we use sensors deployed on a taxi fleet to explore the air quality in the road network of Nanjing over the course of a year (Oct. 2019–Sep. 2020). Based on GIS technology, we develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3). Through hotspots identification analysis, we find three main sources of air pollutants including traffic, industrial emissions, and cooking fumes. We find that CO and NO2 concentrations show a pattern: highways > arterial roads > secondary roads > branch roads > residential streets, reflecting traffic volume. While the O3 concentrations in these five road types are in opposite order due to the titration effect of NOx. Combined the mobile measurements and the stationary station data, we diagnose that the contribution of traffic-related emissions to CO and NO2 are 42.6 % and 26.3 %, respectively. Compared to the pre-COVID period, the concentrations of CO and NO2 during COVID-lockdown period decreased for 44.9 % and 47.1 %, respectively, and the contribution of traffic-related emissions to them both decreased by more than 50 %. With the end of the COVID-lockdown period, traffic emissions and air pollutant concentrations rebounded substantially, indicating that traffic emissions have a crucial impact on the variation of air pollutants levels in urban regions. This research demonstrates the sense power of mobile monitoring for urban air pollution, which provides detailed information for source attribution, accurate traceability, and potential mitigation strategies at urban micro-scale.

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Mobile monitoring of urban air quality at high spatial resolution by low-cost sensors: impacts of COVID-19 pandemic lockdown

Atmospheric Chemistry and Physics ◽

10.5194/acp-21-7199-2021 ◽

2021 ◽

Vol 21 (9) ◽

pp. 7199-7215

Author(s):

Shibao Wang ◽

Yun Ma ◽

Zhongrui Wang ◽

Lei Wang ◽

Xuguang Chi ◽

...

Keyword(s):

Air Quality ◽

Air Pollutants ◽

Low Cost ◽

Air Pollutant ◽

Mitigation Strategies ◽

Traffic Emissions ◽

Urban Air ◽

Industrial Emissions ◽

Gis Technology ◽

Mobile Monitoring

Abstract. The development of low-cost sensors and novel calibration algorithms provides new hints to complement conventional ground-based observation sites to evaluate the spatial and temporal distribution of pollutants on hyperlocal scales (tens of meters). Here we use sensors deployed on a taxi fleet to explore the air quality in the road network of Nanjing over the course of a year (October 2019–September 2020). Based on GIS technology, we develop a grid analysis method to obtain 50 m resolution maps of major air pollutants (CO, NO2, and O3). Through hotspot identification analysis, we find three main sources of air pollutants including traffic, industrial emissions, and cooking fumes. We find that CO and NO2 concentrations show a pattern: highways > arterial roads > secondary roads > branch roads > residential streets, reflecting traffic volume. The O3 concentrations in these five road types are in opposite order due to the titration effect of NOx. Combined the mobile measurements and the stationary station data, we diagnose that the contribution of traffic-related emissions to CO and NO2 are 42.6 % and 26.3 %, respectively. Compared to the pre-COVID period, the concentrations of CO and NO2 during the COVID-lockdown period decreased for 44.9 % and 47.1 %, respectively, and the contribution of traffic-related emissions to them both decreased by more than 50 %. With the end of the COVID-lockdown period, traffic emissions and air pollutant concentrations rebounded substantially, indicating that traffic emissions have a crucial impact on the variation of air pollutant levels in urban regions. This research demonstrates the sensing power of mobile monitoring for urban air pollution, which provides detailed information for source attribution, accurate traceability, and potential mitigation strategies at the urban micro-scale.

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Air quality inside motor vehicles' cabins: A review

Indoor and Built Environment ◽

10.1177/1420326x16679217 ◽

2016 ◽

Vol 27 (4) ◽

pp. 452-465 ◽

Cited By ~ 27

Author(s):

Bin Xu ◽

Xiaokai Chen ◽

Jianyin Xiong

Keyword(s):

Air Quality ◽

Air Pollutants ◽

Ultrafine Particles ◽

High Efficiency ◽

Motor Vehicle ◽

Air Pollutant ◽

Control Measures ◽

Motor Vehicles ◽

Air Filter ◽

Effective Measure

Among many environments, the motor vehicle cabin microenvironment has been of particular public concern. Although commuters typically spend only 5.5% of their time in vehicles, the emissions from various interior components of motor vehicles as well as emissions from exhaust fumes carried by ventilation supply air are significant sources of harmful air pollutants that could lead to unhealthy human exposure due to their high concentrations inside vehicles' cabins. This review summarizes significant findings in the literature on air quality inside vehicle cabins, including chemical species, related sources, measurement methodologies and control measures. More than 90 relevant studies performed across over 10 countries were carefully reviewed. These comprised more than 2000 individual road trips, where concentrations of numerous air pollutants were determined. Ultrafine particles, aromatic hydrocarbons, carbonyls, semi-volatile organic compounds and microbes have been identified as the primary air pollutants inside vehicle cabins. Air recirculation with high-efficiency air filter has been reported as the most effective measure to lower air pollutant concentrations. Future work should focus on investigating the health risks of exposure to various air pollutants inside different vehicles and further developing advanced air filter to improve the in-cabin air quality.

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Evolution of Gaseous and Particulate Pollutants in the Air: What Changed after Five Lockdown Weeks at a Southwest Atlantic European Region (Northwest of Spain) Due to the SARS-CoV-2 Pandemic?

Atmosphere ◽

10.3390/atmos12050562 ◽

2021 ◽

Vol 12 (5) ◽

pp. 562

Author(s):

Jorge Moreda-Piñeiro ◽

Joel Sánchez-Piñero ◽

María Fernández-Amado ◽

Paula Costa-Tomé ◽

Nuria Gallego-Fernández ◽

...

Keyword(s):

Air Quality ◽

Road Traffic ◽

Statistical Significance ◽

Temporal Trends ◽

Air Pollutant ◽

Concentration Data ◽

Southwest Atlantic ◽

Pollutant Concentrations ◽

Air Quality Monitoring Stations ◽

Almost All

Due to the exponential growth of the SARS-CoV-2 pandemic in Spain (2020), the Spanish Government adopted lockdown measures as mitigating strategies to reduce the spread of the pandemic from 14 March. In this paper, we report the results of the change in air quality at two Atlantic Coastal European cities (Northwest Spain) during five lockdown weeks. The temporal evolution of gaseous (nitrogen oxides, comprising NOx, NO, and NO2; sulfur dioxide, SO2; carbon monoxide, CO; and ozone, O3) and particulate matter (PM10; PM2.5; and equivalent black carbon, eBC) pollutants were recorded before (7 February to 13 March 2020) and during the first five lockdown weeks (14 March to 20 April 2020) at seven air quality monitoring stations (urban background, traffic, and industrial) in the cities of A Coruña and Vigo. The influences of the backward trajectories and meteorological parameters on air pollutant concentrations were considered during the studied period. The temporal trends indicate that the concentrations of almost all species steadily decreased during the lockdown period with statistical significance, with respect to the pre-lockdown period. In this context, great reductions were observed for pollutants related mainly to fossil fuel combustion, road traffic, and shipping emissions (−38 to −78% for NO, −22 to −69% for NO2, −26 to −75% for NOx, −3 to −77% for SO2, −21% for CO, −25 to −49% for PM10, −10 to −38% for PM2.5, and −29 to −51% for eBC). Conversely, O3 concentrations increased from +5 to +16%. Finally, pollutant concentration data for 14 March to 20 April of 2020 were compared with those of the previous two years. The results show that the overall air pollutants levels were higher during 2018–2019 than during the lockdown period.

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Quantifying Air Pollutant Variations during COVID-19 Lockdown in a Capital City in Northwest China

Atmosphere ◽

10.3390/atmos12060788 ◽

2021 ◽

Vol 12 (6) ◽

pp. 788

Author(s):

Rong Feng ◽

Hongmei Xu ◽

Zexuan Wang ◽

Yunxuan Gu ◽

Zhe Liu ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Rural Areas ◽

Vehicle Emissions ◽

Motor Vehicle ◽

Northwest China ◽

Air Pollutant ◽

Capital City ◽

Air Pollution Control ◽

Motor Vehicle Emissions

In the context of the outbreak of coronavirus disease 2019 (COVID-19), strict lockdown policies were implemented to control nonessential human activities in Xi’an, northwest China, which greatly limited the spread of the pandemic and affected air quality. Compared with pre-lockdown, the air quality index and concentrations of PM2.5, PM10, SO2, and CO during the lockdown reduced, but the reductions were not very significant. NO2 levels exhibited the largest decrease (52%) during lockdown, owing to the remarkable decreased motor vehicle emissions. The highest K+ and lowest Ca2+ concentrations in PM2.5 samples could be attributed to the increase in household biomass fuel consumption in suburbs and rural areas around Xi’an and the decrease in human physical activities in Xi’an (e.g., human travel, vehicle emissions, construction activities), respectively, during the lockdown period. Secondary chemical reactions in the atmosphere increased in the lockdown period, as evidenced by the increased O3 level (increased by 160%) and OC/EC ratios in PM2.5 (increased by 26%), compared with pre-lockdown levels. The results, based on a natural experiment in this study, can be used as a reference for studying the formation and source of air pollution in Xi’an and provide evidence for establishing future long-term air pollution control policies.

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Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns

Science Advances ◽

10.1126/sciadv.abd6696 ◽

2021 ◽

Vol 7 (3) ◽

pp. eabd6696

Author(s):

Zongbo Shi ◽

Congbo Song ◽

Bowen Liu ◽

Gongda Lu ◽

Jingsha Xu ◽

...

Keyword(s):

Air Quality ◽

Air Pollutant ◽

Pollutant Emissions ◽

Quality Improvements ◽

Learning Technique ◽

Emission Changes ◽

Almost All ◽

Gaseous Oxidant ◽

Sophisticated Analysis ◽

Limited Change

The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO2, O3, and PM2.5 concentrations arising from these emission changes in 11 cities globally by applying a deweathering machine learning technique. Sudden decreases in deweathered NO2 concentrations and increases in O3 were observed in almost all cities. However, the decline in NO2 concentrations attributable to the lockdowns was not as large as expected, at reductions of 10 to 50%. Accordingly, O3 increased by 2 to 30% (except for London), the total gaseous oxidant (Ox = NO2 + O3) showed limited change, and PM2.5 concentrations decreased in most cities studied but increased in London and Paris. Our results demonstrate the need for a sophisticated analysis to quantify air quality impacts of interventions and indicate that true air quality improvements were notably more limited than some earlier reports or observational data suggested.

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Gender Differences in Association between Air Pollution and Daily Mortality in the Capital of the Green Lungs of Poland–Population-Based Study with 2,953,000 Person-Years of Follow-Up

Journal of Clinical Medicine ◽

10.3390/jcm9082351 ◽

2020 ◽

Vol 9 (8) ◽

pp. 2351

Author(s):

Łukasz Kuźma ◽

Krzysztof Struniawski ◽

Szymon Pogorzelski ◽

Hanna Bachórzewska-Gajewska ◽

Sławomir Dobrzycki

Keyword(s):

Public Health ◽

Gender Differences ◽

Air Pollution ◽

Air Quality ◽

Air Pollutant ◽

World Health ◽

The European Union ◽

Daily Mortality ◽

Male Population ◽

The Impact

(1) Introduction: air pollution is considered to be one of the main risk factors for public health. According to the European Environment Agency (EEA), air pollution contributes to the premature deaths of approximately 500,000 citizens of the European Union (EU), including almost 5000 inhabitants of Poland every year. (2) Purpose: to assess the gender differences in the impact of air pollution on the mortality in the population of the city of Bialystok—the capital of the Green Lungs of Poland. (3) Materials and Methods: based on the data from the Central Statistical Office, the number—and causes of death—of Białystok residents in the period 2008–2017 were analyzed. The study utilized the data recorded by the Provincial Inspectorate for Environmental Protection station and the Institute of Meteorology and Water Management during the analysis period. Time series regression with Poisson distribution was used in statistical analysis. (4) Results: A total of 34,005 deaths had been recorded, in which women accounted for 47.5%. The proportion of cardiovascular-related deaths was 48% (n = 16,370). An increase of SO2 concentration by 1-µg/m3 (relative risk (RR) 1.07, 95% confidence interval (CI) 1.02–1.12; p = 0.005) and a 10 °C decrease of temperature (RR 1.03, 95% CI 1.01–1.05; p = 0.005) were related to an increase in the number of daily deaths. No gender differences in the impact of air pollution on mortality were observed. In the analysis of the subgroup of cardiovascular deaths, the main pollutant that was found to have an effect on daily mortality was particulate matter with a diameter of 2.5 μm or less (PM2.5); the RR for 10-µg/m3 increase of PM2.5 was 1.07 (95% CI 1.02–1.12; p = 0.01), and this effect was noted only in the male population. (5) Conclusions: air quality and atmospheric conditions had an impact on the mortality of Bialystok residents. The main air pollutant that influenced the mortality rate was SO2, and there were no gender differences in the impact of this pollutant. In the male population, an increased exposure to PM2.5 concentration was associated with significantly higher cardiovascular mortality. These findings suggest that improving air quality, in particular, even with lower SO2 levels than currently allowed by the World Health Organization (WHO) guidelines, may benefit public health. Further studies on this topic are needed, but our results bring questions whether the recommendations concerning acceptable concentrations of air pollutants should be stricter, or is there a safe concentration of SO2 in the air at all.

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Application of combined model of stepwise regression analysis and artificial neural network in data calibration of miniature air quality detector

Scientific Reports ◽

10.1038/s41598-021-82871-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bing Liu ◽

Qingbo Zhao ◽

Yueqiang Jin ◽

Jiayu Shen ◽

Chaoyang Li

Keyword(s):

Neural Network ◽

Air Quality ◽

Regression Model ◽

Multilayer Perceptron ◽

Stepwise Regression ◽

Control Point ◽

Measurement Data ◽

Environmental Indicators ◽

Air Pollutant ◽

Mlp Model

AbstractIn this paper, six types of air pollutant concentrations are taken as the research object, and the data monitored by the micro air quality detector are calibrated by the national control point measurement data. We use correlation analysis to find out the main factors affecting air quality, and then build a stepwise regression model for six types of pollutants based on 8 months of data. Taking the stepwise regression fitting value and the data monitored by the miniature air quality detector as input variables, combined with the multilayer perceptron neural network, the SRA-MLP model was obtained to correct the pollutant data. We compared the stepwise regression model, the standard multilayer perceptron neural network and the SRA-MLP model by three indicators. Whether it is root mean square error, average absolute error or average relative error, SRA-MLP model is the best model. Using the SRA-MLP model to correct the data can increase the accuracy of the self-built point data by 42.5% to 86.5%. The SRA-MLP model has excellent prediction effects on both the training set and the test set, indicating that it has good generalization ability. This model plays a positive role in scientific arrangement and promotion of miniature air quality detectors. It can be applied not only to air quality monitoring, but also to the monitoring of other environmental indicators.

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On the spread of ultrafine particulate matter: A mathematical model for motor vehicle emissions and their effects as an asthma trigger

International Journal of Biomathematics ◽

10.1142/s179352452150087x ◽

2021 ◽

Author(s):

Michelle N. Rosado-Pérez ◽

Karen Ríos-Soto

Keyword(s):

Mathematical Model ◽

Ultrafine Particles ◽

Motor Vehicle ◽

Traveling Wave Solutions ◽

Reaction Diffusion ◽

Ambient Air ◽

Air Pollutant ◽

Pollutant Emissions ◽

Motor Vehicles ◽

Diffusion Type

Asthma is a respiratory disease that affects the lungs, with a prevalence of 339.4 million people worldwide [G. Marks, N. Pearce, D. Strachan, I. Asher and P. Ellwood, The Global Asthma Report 2018, globalasthmareport.org (2018)]. Many factors contribute to the high prevalence of asthma, but with the rise of the industrial age, air pollutants have become one of the main Ultrafine particles (UFPs), which are a type of air pollutant that can affect asthmatics the most. These UFPs originate primarily from the combustion of motor vehicles [P. Solomon, Ultrafine particles in ambient air. EM: Air and Waste Management Association’s Magazine for Environmental Managers (2012)] and although in certain places some regulations to control their emission have been implemented they might not be enough. In this work, a mathematical model of reaction–diffusion type is constructed to study how UFPs grow and disperse in the environment and in turn how they affect an asthmatic population. Part of our focus is on the existence of traveling wave solutions and their minimum asymptotic speed of pollutant propagation [Formula: see text]. Through the analysis of the model it was possible to identify the necessary threshold conditions to control the pollutant emissions and consequently reduce the asthma episodes in the population. Analytical and numerical results from this work prove how harmful the UFEs are for the asthmatic population and how they can exacerbate their asthma episodes.

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