Mobile Air Quality Monitoring Research Articles

Urban Air Pollution Mapping Using Fleet Vehicles as Mobile Monitors and Machine Learning.

Spatially explicit urban air quality information is important for developing effective air quality control measures. Traditionally, urban air quality is measured by networks of stationary monitors that are not universally available and sparsely sited. Mobile air quality monitoring using equipped vehicles is a promising alternative but has focused on vehicle-level experiments and lacks fleet-level demonstration. Here, we equipped 260 electric vehicles in a ride-hailing fleet in Beijing, China with low-cost sensors to collect real-time, spatial-resolved data on fine particulate matter (PM2.5) concentrations. Using this data, we developed a decision tree model to infer the distribution of PM2.5 concentrations in Beijing at 1 km by 1 km and 1 h resolution. Our results are able to show both short- and long-term variations of urban PM2.5 concentrations and identify local air pollution hotspots. Compared with a benchmark model that only uses data from stationary monitoring sits, our model has shown significant improvement with the coefficient of determination increased from 0.56 to 0.80 and root mean square error decreased from 12.6 to 8.1 μg/m3. To the best of our knowledge, this study collects the largest mobile sensor data for urban air quality monitoring, which are augmented by state-of-the-art machine learning techniques to derive high-quality urban air pollution mapping. Our results demonstrate the potential and necessity of using fleet vehicles as routine mobile sensors combined with advanced data science methods to provide high-resolution urban air quality monitoring.

A new calibration system for low-cost Sensor Network in air pollution monitoring

In recent years, low-cost air sensors designed to measure ambient particulate matters and trace gases have been changing the world of air quality monitoring. Although low-cost sensors have the capability of gathering high temporally and spatially-resolved air quality information, they tend to have more severe data quality issues compared to the conventional monitoring instruments. To enhance the data quality of low-cost sensors, different calibration methods have been developed. In this study, based on the laboratory and field data collected in mainland China, the performances of PM2.5, PM10, CO, NO2, SO2, and O3 sensors were estimated. Then, the sensors were calibrated by different methods and their output data were compared to the reference data to determine the validity and performance of these calibration methods. The results indicated that 1) field calibration with supervised learning technique is an effective technique to decrease the error, and 2) mobile air quality monitoring stations can calibrate the sensors at where standard air quality monitoring stations are not available. In conclusion, a complete laboratory and field calibration system (four-stage calibration) covering the whole life cycle of the sensor is proposed in this paper.

Development of a mobile platform for monitoring gaseous, particulate, and greenhouse gas (GHG) pollutants.

The Michigan Pollution Assessment Laboratory (MPAL) is a mobile air quality monitoring platform designed to measure conventional, toxic, and greenhouse gas (GHG) air pollutants. The spatially and temporally resolved data collected can be used for multiple purposes, such as mapping spatial patterns and identifying peaks. The truck-based platform includes instrumentation for 11 gaseous pollutants and for particulate matter (PM), size distribution (7nm to 20μm), PM10, black and brown carbon, and trace metals. MPAL is equipped with meteorological instruments, a high-accuracy GPS, forward and reverse cameras, and a data logging and display system. We selected commercially available instrumentation based on sensitivity, response time, and robustness. The vehicle's power system allows ~ 6.5h of continuous operation with all instruments operating. This article details the design, construction, and evaluation of MPAL and summarizes data collected in its first year (March 2019 to March 2020) of operation. We completed a series of runs on 84days in Detroit, Michigan, an area with a diverse set of traffic, industrial, and commercial emission sources, and collected 265,816 1-s observations (excluding collocations, zero checks, and other quality assurance measurements). Using data from these runs as well as special tests, we present results of performance evaluations that examined the response time, PM losses, and wind measurements and compare results to stationary regulatory monitoring data. We highlight key issues and provide practical solutions to help evaluate and resolve these issues and share many lessons learned in developing and using a mobile platform.

Air quality monitoring using mobile low-cost sensors mounted on trash-trucks: Methods development and lessons learned

Air quality monitoring (AQM) is crucial for cities to develop management plans supporting population health. However, there is a dearth of measurements due to the high cost of standard reference instruments. Mobile AQM using low-cost sensors deployed on routine fleets of vehicles can enable the continuous detection of fine-scale pollutant variations in cities at a lower cost. New methods need to be developed to interpret these measurements. This paper presents three such methods. First, we propose a technique to identify aerosol hotspots. Second, we employ techniques published previously to assess the generalizable map of fine and coarse particle number concentrations, to understand qualitatively the contribution of local and regional sources across the region sampled. By using the raw number concentration of differently sized particles from the Optical Particle Counters (OPCs) instead of the noisier mass concentrations, we obtain more robust results. Third, in order to evaluate source signatures in cities, we propose another technique, in which we cluster the entire range of aerosol size-distribution measurements acquired. The properties of each cluster provide insight into the aerosol source characteristics in the sampling environment. We test these methods using a dataset we collected by mounting OPCs on two trash-trucks in Cambridge, Massachusetts.

Distinguish Potential Source Areas of PM2.5 and PM10 by Statistical Data Analysis

Power plant from biomass in community area has been complained as emission source of many pollutants such as wastewater, noise, odor and polluted air. For air pollution, total suspended particulate (TSP) and particulate matter less than 10 micron (PM10) from rice husk energy process is quite large. Roi Et is a province located in Northeast of Thailand which obtains power plants using rice husk as fuel in the process. According to the land-use pattern of the community area of Roi Et, it revealed that there are many sources of air pollution. This study selected the statistical techniques to distinguish the potential source area influencing the high concentrations of particulate matter less than 2.5 micron (PM2.5), particulate matter 2.5 to 10 micron (PMcoarse), and PM10 which caused the health effects of people around the rice husk power plants. Hourly observed PM10 and PM2.5 data were analysed by time series and bivariate polar plot. Results showed that increasing the concentration of PM10 and PM2.5 were observed in the duration of low wind speed. However, daily average of PM10 was not more than National Ambient Air Quality Standard (NAAQS) which is 120 μg/m3. In contrast, results showed that daily average of PM2.5 performed over NAAQS of 50 μg/m3 in the 20th -22nd December 2018 that high pressure cover Thailand. While sources of these particulate matters could be distinguished into 3 potential areas based on the mobile air quality monitoring station location which were 1) east side and 2) north-west side which regarded as sub-urban and traffic areas, respectively and 3) north-east side which regarded as power plants area. Results revealed that PMcoarse was the major pollutant dispersed from the Northeast direction that the power plants were the major source. Whereas traffic and community were the main sources of PM2.5 coming from the East and the Northwest direction of the mobile air quality monitoring station point. Therefore, discrimination of each point source could be done first before implementing to control and manage the air pollution effectively.

Real-Time Monitoring of Indoor Air Quality with Internet of Things-Based E-Nose

Today, air pollution is the biggest environmental health problem in the world. Air pollution leads to adverse effects on human health, climate and ecosystems. Air is contaminated by toxic gases released by industry, vehicle emissions and the increased concentration of harmful gases and particulate matter in the atmosphere. Air pollution can cause many serious health problems such as respiratory, cardiovascular and skin diseases in humans. Nowadays, where air pollution has become the largest environmental health risk, the interest in monitoring air quality is increasing. Recently, mobile technologies, especially the Internet of Things, data and machine learning technologies have a positive impact on the way we manage our health. With the production of IoT-based portable air quality measuring devices and their widespread use, people can monitor the air quality in their living areas instantly. In this study, e-nose, a real-time mobile air quality monitoring system with various air parameters such as CO2, CO, PM10, NO2 temperature and humidity, is proposed. The proposed e-nose is produced with an open source, low cost, easy installation and do-it-yourself approach. The air quality data measured by the GP2Y1010AU, MH-Z14, MICS-4514 and DHT22 sensor array can be monitored via the 32-bit ESP32 Wi-Fi controller and the mobile interface developed by the Blynk IoT platform, and the received data are recorded in a cloud server. Following evaluation of results obtained from the indoor measurements, it was shown that a decrease of indoor air quality was influenced by the number of people in the house and natural emissions due to activities such as sleeping, cleaning and cooking. However, it is observed that even daily manual natural ventilation has a significant improving effect on air quality.

Creating environmental consciousness in underserved communities: Implementation and outcomes of community-based environmental justice and air pollution research

While the literature documents known barriers to research participation in underserved communities, there are still very few evidence-based strategies that have successfully addressed gaps regarding recruitment and retention. To this end, we developed a replicable model designed to establish community-academic partnerships and advance environmental health research in underserved communities. The Environmental Justice Community Alert Matrix (EJCAM) was implemented in Pittsburgh, Pennsylvania, and includes programmatic elements of both citizen science and community engagement: (1) Outreach – Community Action Teams, (2) Involvement – Urban Transition Cities Movement, (3) Participatory Research – Mobile Air Quality Monitoring Bicycle Campaign, and (4) Consultation – Indoor Air Quality Assessments. Resident-led workshops were used to increase environmental consciousness in communities through peer-to-peer education. Adapting citizen science methods, commercially available particle counters were retrofitted to bicycles and the associated data used to develop GIS maps of particulate matter dispersion. Results show spatially-resolved PM2.5 values increase from background levels by an order of magnitude or 1000 counts adjacent to construction sites and public transportation hubs. As distrust in outside institutions has limited the reach of environmental justice research in underserved communities, bottom-up principles that start with individuals are essential, and should be undertaken as a meaningful and ongoing process.

State of air quality in twin cities of Pakistan: Islamabad and Rawalpindi

Atmospheric pollution has emerged as a disaster in developing countries like Pakistan. The aim of this study was to observe and analyze the air quality status of the twin cities of Pakistan, Rawalpindi and Islamabad, with a combined population of 15 million. The concentration of criteria pollutants along with CO 2 was measured at five different locations within these cities. Sampling was done for 24 h at each location using a mobile air quality monitoring lab. The overall concentrations of SO 2 , NO X and O 3 were found to be within the permissible limits of the US-EPA standards and the National Environmental Quality Standards of Pakistan, with higher PM 10 and CO 2 concentrations at most locations. The highest concentrations of PM 10 were observed at Saddar, Rawalpindi (184 µg m –3 ), while minimum values were observed at the Blue Area of Islamabad (121 µg m –3 ). The concentration of CO 2 during the study period was found to be high in almost all locations, with Saddar, Rawalpindi, indicating the highest value (409 ppm). The concentrations of CO 2 at the Blue Area and the National University of Science and Technology of Islamabad were observed to be as high as 385.3 and 246 ppm, respectively. The behavior of pollutants with different meteorological parameters was also studied. For source identification, backward air trajectories were also generated using the HYSPLIT model.

Spatial-temporal Assessment and Mapping of the Air Quality and Noise Pollution in a Sub-area Local Environment inside the Center of a Latin American Megacity: Universidad Nacional de Colombia - Bogotá Campus

The construction, development and maintenance of an economically, environmentally and socially sustainable campus involves the integration of measuring tools and technical information that invites and encourages the community to know the actual state to generate positive actions for reducing the negative impacts over the local environment. At the Universidad Nacional de Colombia - Campus Bogotá, a public area with daily traffic of more than 25000 people, the Environmental Management Bureau has committed with the monitoring of the noise pollution and air quality, as support to the campaigns aiming to reduce the pollutant emissions associated to the student’s activities and campus operation. The target of this study is based in the implementation of mobile air quality and sonometry monitoring equipment, the mapping of the actual air quality and noise pollution inside the university campus as a novel methodology for a sub-area inside a megacity. This results and mapping are proposed as planning tool for the institution administrative sections. A mobile Kunak® Air & OPC air monitoring station with the capability to measure particulate matter PM10, PM25, Ozone (O3), Sulfur Oxide (SO2), Carbon Monoxide (CO) and Nitrogen Oxide (NO2) as well as Temperature, Relative Humidity and Latitude and Longitude coordinates for the data georeferenciation; and a sonometer Cirrus® 162B Class 2 were used to perform the measurements. The measurements took place in conditions of academic activity and without it, with the aim of identify the impacts generated by the campus operation. Using the free code geographical information software QGIS® 2.18, the maps of each variable measured were developed, and the impacts generated by the operation of the campus were identified qualitative and quantitively. For the measured variables, an increase of around 21% for the LAeq noise level and around 80% to 90% for air pollution were detected during the operation period.

High spatiotemporal characterization of on-road PM2.5 concentrations in high-density urban areas using mobile monitoring

Mobile air quality monitoring reports air pollutant concentrations at a high spatiotemporal resolution, enabling the characterization of heterogeneous human exposure and localized pollution hotspots. In this study, on-road concentrations of fine particulate matter (PM2.5) in a high-density urban area in Hong Kong were measured in December 2014 and January 2015 (winter) and June and July 2015 (summer) using a tramcar mobile monitoring platform. We developed a method of mapping the winter and summer on-road PM2.5 concentrations along a tramcar route at a 50-m spatial resolution, using mobile measurements. In addition, the minimum number of days required to precisely estimate on-road PM2.5 concentrations was estimated. The results showed that the on-road PM2.5 concentrations were highly correlated with PM2.5 concentrations measured at a nearby roadside air quality monitoring station (AQMS) in both winter and summer, with Pearson correlation coefficients of 0.89–0.93. The resulting maps of winter and summer on-road PM2.5 concentrations revealed small-scale spatial patterns used to identify more polluted areas. In addition, approximately 12 and 4 days were required to precisely capture spatial patterns of PM2.5 concentrations, with R2 higher than 0.6 in winter and in summer. The findings of this study offer valuable information on air pollution control and exposure reduction by highlighting localized pollution hotspots, and provide insights into the minimum sampling duration for mobile sampling campaigns.

Characterization of NOx-Ox relationships during daytime interchange of air masses over a mountain pass in the Mexico City megalopolis

The role of the Tenango del Aire mountain pass, located southeast of the Mexico City Metropolitan Area (MCMA), in venting the city's air pollution has already been studied from a meteorological standpoint. To better understand the transport of gaseous air pollutants through the Tenango del Aire Pass (TAP), and its influence on the air quality of the MCMA, three mobile air quality monitoring units were deployed during a 31-day field campaign between February and March of 2011.Surface O3, NOx, and meteorological variables were continuously measured at the three sites. Vertical profiles of O3 and meteorological variables were also obtained at one of the sites using a tethered balloon. Days were classified as being under low pressure synoptic systems (LPS, 13 days), high pressure synoptic systems (HPS, 13 days), or as transition days (TR). The Mexican ozone standards at the Pass were not exceeded during LPS days, but were exceeded on almost all HPS days.A detailed analysis was performed using data from two typical days, one representative of LPS and the other of HPS. In both cases, morning vertical profiles of O3 showed a strong thermal inversion layer and near-surface O3 titration due to fresh NOx. In the LPS early morning, a single O3 layer of close to 45 ppb was observed from 150 to 700 magl. In the HPS early morning, 50 ppb was observed from 150 to 400 magl followed by a 400-m-thick layer with up to 80 ppb. These layers were the source of the morning increase of O3, with a simultaneous sharp decrease of NOx and CO as the mixing layer started to rise.During the LPS day, a southerly wind dominated throughout most of the daytime, with surface O3 lower than 60 ppb. The same was observed for the well-mixed midday and afternoon vertical profiles. Under HPS, northerly winds transported photochemically active air masses from the MCMA all morning, as observed by a smoother increase of Ox and O3, reaching 110 ppb of O3. Just after midday, the wind shifted back, carrying high-O3 (100–110 ppb) aged air masses until sunset. In addition, the midday and afternoon vertical profiles showed well-mixed high-O3 (100–110 ppb) mixing ratios.Analysis of Ox-NOx correlations was performed for these peri-urban and MCMA sites. A parallel analysis for the nearest urban air quality monitoring station in the MCMA was also done. A comparison allowed us to distinguish between photochemically active (VOC sensitive) or aged parcels (NOx sensitive) arriving at the TAP. Separating the correlations into time groups associated with wind direction changes allowed us to better distinguish between local, MCMA, or regional influence. The results are relevant to air quality management in the Mexico City megalopolis.

The Islandwide Ambient Air Quality Monitoring Program in Sri Lanka

In recent decades, the combination of growth of major cities across the country, increased use of vehicles, rapid industrialization, and deficiencies in both planning and environmental regulations, has led to increasing the levels of air pollution and consequent health and environmental impacts. The observed levels of criteria air pollutants (CAPs) and hazardous air pollutants (HAPs) often reach magnitudes that are considered to be a threat to human health. Ambient concentrations of gaseous pollutants and fine particles are stable or even increasing over time. Being a responsible key organization in Sri Lanka, Central Environmental Authority has been engaged with an urban air quality monitoring program across the country since 2014 under financial sponsorship of the vehicle emission trust fund of the department of motor traffic. In this program, the criteria air pollutants such as PM2.5, PM10, SO2, NOx, CO, O3 and Non methane volatile organic compounds (NMVOCs) are monitored in major cities. The 24-hour continuous monitoring for seven days in each and every city are accomplished by using fully automated mobile ambient air quality monitoring station. As observed, the recorded average concentrations of each parameter, in major cities are maintained below the National ambient air quality standards. But it was found that there is a little increasing trend of PM2.5 and PM10 in major cities compared to other pollutants. It is also notable that the average concentrations of other parameters such as SO2, NO2, CO and O3 have been recorded below the National ambient air quality standards. However, these values are defined to 24-hour continuous monitoring for 07 days and it is an instant situation on ambient air quality in the cities considered. These pollutant concentrations have been given for 24-hour consecutive 07 days for a particular city and once a year monitoring for a particular season. Hence the data is very limited and not representative for the entire year. A large set of continuous monitoring data representing all seasons in Sri Lanka is also requires in order to introduce a standard methodology at the data processing. Further an improvement of current ambient air quality monitoring program is recommended in Sri Lanka taking into account the drawbacks identified. Keywords: Ambient air quality, Criteria air pollutants, Hazardous air pollutants, National ambient air quality standards

Continuous measurement of atmospheric reduced sulphur compounds as key tracers between odour complaints and source apportionment.

Odour complaints are frequent nowadays, particularly nearby industrial facilities emitting odorous compounds. Among all compounds susceptible of causing odour annoyance, reduced sulphur compounds (RSC) were studied, due to their unpleasant odour and low odour threshold. RSC ambient air mixing ratios, meteorological conditions and population complaints were analysed in an area of complex topography in order to identify their potential sources. Mixing ratios of three compounds, dimethyl sulphide (DMS), carbon disulphide (CDS) and dimethyl disulphide (DMDS), were continuously monitored using an online gas chromatograph coupled with a mass spectrometer detector (GC-MSD), which was placed in a mobile air quality monitoring unit. Measurement campaigns were performed during 2012 and 2013 for periods of 7-25days in an urban area (four campaigns, N=1368) and an urban area surrounded by industrial activities (three campaigns, N=564). During such campaigns, RSC mixing ratios were frequently above their odour thresholds, which did not always involve citizen complaints. Average RSC ambient air mixing ratios tended to be lower in the urban area (DMS 0.06-0.33, CDS 0.05-0.10, DMDS 0.07-0.30μgm-3) than in the industry surrounded one (DMS 0.30-2.39, 0.05-0.18, DMDS 0.09-0.62μgm-3). The DMS/DMDS mixing ratio was frequently above 1, being a paper mill one of the main sources of RSC in the area. DMS/DMDS ratios below 1 were also recorded, suggesting a waste treatment plant as the RSC source or older air masses coming from the paper mill.

Assessment of spatial variations of particulate matter (PM10 and PM2.5) in Bahrain identified by air quality index (AQI)

The rapid urbanization, industrialization, modernization, and the frequent Middle Eastern dust storms have negatively impacted the ambient air quality in Bahrain. The objective of this study is to identify the most critical atmospheric air pollutants with emphasis on their potential risk to health based on calculated AQI (air quality index) values using EPA approach. The air quality datasets of particulate matters (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) were measured in January 2012 and August 2012 using five mobile air quality monitoring stations located at different governorates. The results of this study demonstrated that PM10 and PM2.5 are the most critical air pollutants in Bahrain with PM2.5 prevailing during January 2012 and PM10 prevailing during August 2012. The corresponding AQI categories were utilized to evaluate spatial variability of particulate matters in five governorates. The impact of meteorological factors such as ambient air temperature, wind speed, relative humidity, and total precipitation on ambient air quality were discussed. The analysis demonstrated that the highest PM10 concentrations were observed in the Northern Governorate while the highest PM2.5 concentrations were observed in the Capital, Central, and Northern Governorates during August 2012. It was observed that the levels of PM2.5 pollution were higher within proximity of the industrial zone. The results suggested that the average PM2.5/PM10 ratio in August 2012 was lower than in January 2012 due to the Aeolian processes. This study concludes that higher wind speed, total precipitation, relative humidity rates, and lower ambient air temperature in January 2012 assisted with the dissipation of particulate matter thus lowering the pollution levels of both PM10 and PM2.5 in comparison to August 2012.

Lessons Learned from Extensive Mobile Air Quality Monitoring with Google Street View Cars

Introduction: Urban concentrations of air pollutants such as ultrafine particles and black carbon vary sharply over fine spatial scales (~ 1 – 300 m). This spatial variation is not usually well-represented by routine fixed-site observations. Data from mobile air quality monitoring platforms can contribute to a richer understanding of intraurban variation in air pollution; however, previous mobile monitoring campaigns have deployed few repeat measurements. Goals of this analysis are to (1) investigate the stability of spatial trends detected by mobile monitoring and (2) map fine scale spatial variability of air quality. Methods: We collected an extensive dataset of routine mobile air quality measurements in the San Francisco Bay Area using a fleet of Google Street View cars with fast-response (1 Hz) particle instrumentation. These vehicles collect 40-60 h per week of daytime air quality measurements, including ultrafine particle number count (PN) and black carbon (BC). The dataset presented incorporates >1200 h and >28,000 km of on-road data collected from May-Dec 2015. Within our core sampling region, we made ≥ 20-40 sampling trips along every public street, yielding ~20,000 repeatedly sampled 30 m road segments. We systematically sub-sample this dataset to investigate how many samples are required to develop stable estimates of long-term spatial trends. Results: Preliminary results indicate BC and PN medians for 30 m road segments have high correlation (R-Squared ≥ 0.75) with their long term medians after 10 and 15 drives, respectively. Over all driving, median BC and PN concentrations were roughly twice as high on highways as on major arterial roads, and twice as high on arterials as on residential streets. Conclusions: Our results on the stability of long-term medians will help inform future plans for mobile monitoring air quality campaigns. PN and BC are substantially higher on highways compared to major arterial roads, which are higher than residential roads.

A real-time, mobile air quality monitoring platform for developing spatial models

A real-time, mobile air quality monitoring platform for developing spatial models

Investigation of fine particulate pollution in a coastal city with a mobile monitoring platform

Abstract Spatial and temporal variation of fine particulate pollution in a complex small coastal city, Macau, was investigated by using a mobile air quality monitoring platform. Real-time street level measurements of aerosol size distribution were carried out three times per day by using an on-board wide-range particle spectrometer along a designed route covering the main area of the city for one week. The measured distributions were then transformed into PM2.5 mass concentrations for assessment based on an empirical relation estimated from a co-located measurement calibration process. Within this week, the averaged PM2.5 mass concentrations of the morning, afternoon, and evening sessions are 51.76 μg m-3, 28.87 μg m-3 and 30.77 μg m-3, respectively. By comparing the spatial statistics of the estimated PM2.5 concentrations with the US 24-hour primary NAAQS as a reference, a significant probability of exceedance (> 86%) was observed in 1/3 of the monitoring sessions, particularly in the morning of high traffic volume. Meanwhile, majority of the hotspots were found in the districts of the Macau peninsula. In addition, by performing analysis of the backward trajectories, daily MODIS images, and comparison of the PM2.5 patterns of the entire route of three selected measuring sessions during weekdays and weekend, it was observed that long range transport of fine particulates may affect Macau in a larger scale. Therefore, regional land source influence could shadow the local one when the ambient conditions are met.

The Aeroflex: a bicycle for mobile air quality measurements.

Fixed air quality stations have limitations when used to assess people's real life exposure to air pollutants. Their spatial coverage is too limited to capture the spatial variability in, e.g., an urban or industrial environment. Complementary mobile air quality measurements can be used as an additional tool to fill this void. In this publication we present the Aeroflex, a bicycle for mobile air quality monitoring. The Aeroflex is equipped with compact air quality measurement devices to monitor ultrafine particle number counts, particulate mass and black carbon concentrations at a high resolution (up to 1 second). Each measurement is automatically linked to its geographical location and time of acquisition using GPS and Internet time. Furthermore, the Aeroflex is equipped with automated data transmission, data pre-processing and data visualization. The Aeroflex is designed with adaptability, reliability and user friendliness in mind. Over the past years, the Aeroflex has been successfully used for high resolution air quality mapping, exposure assessment and hot spot identification.

Influence of Sea-Land Breezes on the Tempospatial Distribution of Atmospheric Aerosols over Coastal Region

ABSTRACT The influence of sea-land breezes (SLBs) on the spatial distribution and temporal variation of particulate matter (PM) in the atmosphere was investigated over coastal Taiwan. PM was simultaneously sampled at inland and offshore locations during three intensive sampling periods. The intensive PM sampling protocol was continuously conducted over a 48-hr period. During this time, PM2.5 and PM2.5–10 (PM with aerodynamic diameters ≤2.5 μm and between 2.5 and 10 μm, respectively) were simultaneously measured with dichotomous samplers at four sites (two inland and two offshore sites) and PM10 (PM with aerodynamic diameters ≤10 μm) was measured with β-ray monitors at these same 4 sites and at 10 sites of the Taiwan Air Quality Monitoring Network. PM sampling on a mobile air quality monitoring boat was further conducted along the coastline to collect offshore PM using a β-ray monitor and a dichotomous sampler. Data obtained from the inland sites (n = 12) and offshore sites (n = 2) were applied to plot the PM10 concentration contour using Surfer software. This study also used a three-dimensional meteorological model (Pennsylvania State University/National Center for Atmospheric Research Meteorological Model 5) and the Comprehensive Air Quality Model with Extensions to simulate surface wind fields and spatial distribution of PM10 over the coastal region during the intensive sampling periods. Spatial distribution of PM10 concentration was further used in investigating the influence of SLBs on the transport of PM10 over the coastal region. Field measurement and model simulation results showed that PM10 was trans ported back and forth across the coastline. In particular, a high PM10 concentration was observed at the inland sites during the day because of sea breezes, whereas a high PM10 concentration was detected offshore at night because of land breezes. This study revealed that the accumulation of PM in the near-ocean region because of SLBs influenced the tempospatial distribution of PM10 over the coastal region. IMPLICATIONS Model simulation and field measurements show that PM was transported back and forth across the coastline because of SLBs and that this regularly influenced the tempospatial distribution of PM10 over the coastal region. A high PM10 concentration was observed at the inland sites because of daytime onshore breezes, whereas a high PM10concentration was detected over the ocean because of nighttime offshore breezes. The implications of these results suggest that relevant authorities should work to prevent pollution episodes by reducing regional PM emissions that lead to stagnant or recirculating air under specific weather conditions.

Comparison of predicted and observed PM10 concentrations in several urban street canyons

This paper presents an evaluation of a street canyon model (Operational Street Pollution Model) in several urban streets having different configurations. The model was performed for the prediction of particulate matter (PM10) concentrations from exhaust emissions of mobile sources in five street canyons in the city of Izmir, Turkey. Hourly concentrations of PM10 were observed at the streets and the relevant hourly meteorological parameters were measured at the roof level. The hourly street level measurements by a mobile ambient air quality monitoring station and on-site automatic traffic counts were conducted for 1 week in each street during the period of November 2007 and March 2008. The urban background concentrations were also obtained from four stationary air quality monitoring stations in the city during the measurement campaigns and they were included in the modeling studies as the contribution of background air quality. Finally, statistical analyses were carried out to evaluate the model performance by comparing the predicted and observed time series of PM10 concentrations using a correlation coefficient and an index of agreement (IA). The IA varied from 0.87 to 0.98 at the symmetric canyons and the correlation coefficient ranged from 0.68 to 0.92, indicating modeling performances ranging from acceptable to very good. The similar values were calculated between 0.74 and 0.76 for IA and between 0.34 and 0.41 for correlation coefficient at the asymmetric canyons. The best agreement between predicted and observed PM10 concentrations (IA = 0.98, R2 = 0.92) was found for Cumhuriyet Avenue in this study. These values are found as the best agreement in overall studies in literature.