Mobile Measurement of Particulate Matter Concentrations on Urban Streets: System Development and Field Verification

Concentrations of various particulate matter (PM) in urban areas have attracted great attention, due to the increasing demand on life quality. Many studies have highlighted the spatial variability of PM<sub>2.5</sub> in urban areas, and found that there are significant differences between residents’ exposure and background levels. Different from the strategy of establishing large-scale Airbox stations or utilizing mobile stations with portable instruments to measure residents’ exposures, this study develops an on-vehicle monitoring system (OVMS), which is based on the technology of Internet of Things, to increase the spatial resolution of the monitoring data in an economical way. The parameters measured by the OVMS include PM<sub>2.5</sub>, time, location, moving speed, ambient temperature, and relative humidity. According to the experimental results, the effects of the moving speed of the OVMS on PM<sub>2.5</sub> measurements are negligible (<inline-formula> <tex-math notation="LaTeX">$r =0.024$ </tex-math></inline-formula>), when the moving speed is below 57 km/hr. The correlation between the dynamic measurements provided by the OVMS and a standard instrument is high (<inline-formula> <tex-math notation="LaTeX">$r =0.601$ </tex-math></inline-formula>). These results show that the OVMS can accurately monitor PM<sub>2.5</sub> as it moves. The data of PM<sub>2.5</sub> obtained by the OVMS also reveal the impacts of traffic and community pollution in urban areas on residents’ exposure. In addition, this study proposes a visualized map that shows real-time PM<sub>2.5</sub> measurements as the OVMS travels. Map users can choose a less-polluted path to get to their destinations based on the PM<sub>2.5</sub> information. In addition, the OVMS measurements can be integrated with the Airbox measurements, so the visualized map can provide detailed spatial interpolation results on PM<sub>2.5</sub> exposures. Thus, the OVMS can be a great help in evaluating the PM2.5 levels in certain areas of urban streets where Airbox stations are not installed.

[1]  S. Lung,et al.  Inequality of Asian-type neighborhood environmental quality in communities with different urbanization levels , 2014 .

[2]  Alan M. Jones,et al.  Field study of the influence of meteorological factors and traffic volumes upon suspended particle mass at urban roadside sites of differing geometries , 2004 .

[3]  Hualiang Lin,et al.  Short-term and long-term exposures to fine particulate matter constituents and health: A systematic review and meta-analysis. , 2019, Environmental pollution.

[4]  W James Gauderman,et al.  Childhood Asthma and Exposure to Traffic and Nitrogen Dioxide , 2005, Epidemiology.

[5]  Tukur Dahiru,et al.  P – VALUE, A TRUE TEST OF STATISTICAL SIGNIFICANCE? A CAUTIONARY NOTE , 2008, Annals of Ibadan postgraduate medicine.

[6]  D. Leith.  Light Scattering Theory , 2018 .

[7]  N Künzli,et al.  Public-health impact of outdoor and traffic-related air pollution: a European assessment , 2000, The Lancet.

[8]  Sachit Mahajan,et al.  ADF: An Anomaly Detection Framework for Large-Scale PM2.5 Sensing Systems , 2018, IEEE Internet of Things Journal.

[9]  Bert Brunekreef,et al.  Association between mortality and indicators of traffic-related air pollution in the Netherlands: a cohort study , 2002, The Lancet.

[10]  Bart Elen,et al.  The Aeroflex: A Bicycle for Mobile Air Quality Measurements , 2012, Sensors.

[11]  R. Burnett,et al.  Spatial Analysis of Air Pollution and Mortality in Los Angeles , 2005, Epidemiology.

[12]  Patrick Berghmans,et al.  Monitoring PM10 and Ultrafine Particles in Urban Environments Using Mobile Measurements , 2013 .

[13]  Christoph Schneider,et al.  Mobile measurements and regression modeling of the spatial particulate matter variability in an urban area. , 2012, The Science of the total environment.

[14]  K. Pinkerton,et al.  Pulmonary health effects of air pollution , 2016, Current opinion in pulmonary medicine.

[15]  B. Chu,et al.  II – LIGHT SCATTERING THEORY , 1991 .

[16]  Kiros Berhane,et al.  Childhood Incident Asthma and Traffic-Related Air Pollution at Home and School , 2010, Environmental health perspectives.

[17]  Joris Van den Bossche,et al.  Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset , 2015 .

[18]  M. Mukaka,et al.  Statistics corner: A guide to appropriate use of correlation coefficient in medical research. , 2012, Malawi medical journal : the journal of Medical Association of Malawi.

[19]  Shih-Chun Candice Lung,et al.  Variability of intra-urban exposure to particulate matter and CO from Asian-type community pollution sources , 2014 .

[20]  Wei Dong,et al.  Mosaic: A low-cost mobile sensing system for urban air quality monitoring , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[21]  Haiying Liu,et al.  A Review of Airborne Particulate Matter Effects on Young Children’s Respiratory Symptoms and Diseases , 2018 .

[22]  Li-Shan Huang,et al.  Analysis of variance, coefficient of determination and $F$-test for local polynomial regression , 2008, 0810.4808.

[23]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

[24]  Jennifer K Vanos,et al.  Low-cost mobile air pollution monitoring in urban environments: a pilot study in Lubbock, Texas , 2018, Environmental technology.

[25]  Liviu Iftode,et al.  Real-time air quality monitoring through mobile sensing in metropolitan areas , 2013, UrbComp '13.

[26]  Bert Brunekreef,et al.  Air pollution from traffic and the development of respiratory infections and asthmatic and allergic symptoms in children. , 2002, American journal of respiratory and critical care medicine.

[27]  Jayant M. Pinto,et al.  Effects of Ambient Air Pollution Exposure on Olfaction: A Review , 2016, Environmental health perspectives.

[28]  Abdellatif Kobbane,et al.  MoreAir: A Low-Cost Urban Air Pollution Monitoring System , 2020, Sensors.

[29]  R. Burnett,et al.  Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. , 2002, JAMA.

[30]  Paolo Bellavista,et al.  Mobeyes: smart mobs for urban monitoring with a vehicular sensor network , 2006, IEEE Wireless Communications.

[31]  Stephan Weber,et al.  Flow characteristics and particle mass and number concentration variability within a busy urban street canyon , 2006 .

[32]  Yu Zheng,et al.  U-Air: when urban air quality inference meets big data , 2013, KDD.

[33]  Annette C. Rohr,et al.  Attributing health effects to individual particulate matter constituents , 2012 .

[34]  D. Gu,et al.  Air pollution, economic development of communities, and health status among the elderly in urban China. , 2008, American journal of epidemiology.

[35]  Wenting Han,et al.  Unmanned Aerial Vehicle-Borne Sensor System for Atmosphere-Particulate-Matter Measurements: Design and Experiments , 2019, Italian National Conference on Sensors.

[36]  Ling-Jyh Chen,et al.  An Open Framework for Participatory PM2.5 Monitoring in Smart Cities , 2017, IEEE Access.

[37]  D. Shepard.  A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

[38]  M. Stafoggia,et al.  Socioeconomic status, particulate air pollution, and daily mortality: differential exposure or differential susceptibility. , 2006, American journal of industrial medicine.

[39]  R. Belarbi,et al.  Modelling air flows around buildings in urban environment , 2006 .