Air Pollution Mapping Research Articles

A comparison of associations with childhood lung function between air pollution exposure assessment methods with and without accounting for time-activity patterns

BackgroundTo investigate associations between annual average air pollution exposures and health, most epidemiological studies rely on estimated residential exposures because information on actual time-activity patterns can only be collected for small populations and short periods of time due to costs and logistic constraints. In the current study, we aim to compare exposure assessment methodologies that use data on time-activity patterns of children with residence-based exposure assessment. We compare estimated exposures and associations with lung function for residential exposures and exposures accounting for time activity patterns. MethodsWe compared four annual average air pollution exposure assessment methodologies; two rely on residential exposures only, the other two incorporate estimated time activity patterns. The time-activity patterns were based on assumptions about the activity space and make use of available external data sources for the duration of each activity. Mapping of multiple air pollutants (NO2, NOX, PM2.5, PM2.5absorbance, PM10) at a fine resolution as input to exposure assessment was based on land use regression modelling. First, we assessed the correlations between the exposures from the four exposure methods. Second, we compared estimates of the cross-sectional associations between air pollution exposures and lung function at age 8 within the PIAMA birth cohort study for the four exposure assessment methodologies. ResultsThe exposures derived from the four exposure assessment methodologies were highly correlated (R > 0.95) for all air pollutants. Similar statistically significant decreases in lung function were found for all four methods. For example, for NO2 the decrease in FEV1 was −1.40% (CI; −2.54, −0.24%) per IQR (9.14 μg/m3) for front door exposure, and −1.50% (CI; −2.68, −0.30%) for the methodology which incorporates time activity pattern and actual school addresses. ConclusionsExposure estimates from methods based on the residential location only and methods including time activity patterns were highly correlated and associated with similar decreases in lung function. Our study illustrates that the annual average exposure to air pollution for 8-year-old children in the Netherlands is sufficiently captured by residential exposures.

ON THE ORGANIZATION AND VALIDATION OF A PILOT TEST OF A MOBILE CROWDSOURCED AIR QUALITY MONITORING SYSTEM

Abstract. The development of new tools that allow continuous monitoring of air quality is essential for the study of actions, in order to improve the levels of pollutants in the air that are harmful to the health of citizens. Cardiovascular and respiratory diseases have been identified as risk factors for death in patients with COVID-19; at the same time, exposure to air pollution is associated with these diseases. In this article, we present the pilot tests of the Crowdsourced Air Quality Monitoring (C-AQM) system, which allows the generation of reliable air pollution maps, using data provided by low-cost sensor nodes. The results verify that the system is correct after performing a data calibration; an improvement in NO2 pollution has been observed on weekends, as well as a situation of less air pollution by NO2 between the first and second pandemic waves in Spain.

Assessment of Environmental Hazards to Public Health in Temperate Urban Argentina.

Human health risk in urban areas depends on multiple environmental features. We performed a year-round survey in a highly urbanized district located in temperate Argentina (General San Martín, Buenos Aires) to establish baseline information about environmental hazards associated with health risks. Sampling was stratified into low and high hazardous zones according to estimated indexes previously developed for the area for four hazards: drinking water and air pollution, and mosquito and rodent infestation. Water from wells showed lower concentrations of aluminum, manganese and iron, and higher values of arsenic than tap samples, with the latter showing records above the maximum permitted for arsenic, aluminum and chromium. Benzene concentration in air was higher in summer than in winter, and in areas close to dumps and landfills, gas stations, high traffic pathways and industries with respect to low hazard areas. Adult mosquito collections were more abundant in high hazardous areas, three species from the genus Culex dominated the captures and the proportion of individuals from each species was variable seasonally and spatially. Rodent activity was recorded inside and outside dwellings, and its observed values did not differ between low and high hazardous areas. In the comparison between field data and estimated hazard maps, high accuracy was obtained for air pollution maps, intermediate accuracy for water pollution and mosquito infestation, and poor accuracy for rodent infestation. How to improve field surveys and estimated maps are both discussed, highlighting the need for dynamic feedback between GIS-based models and environmental monitoring.

The winter 2019 air pollution (PM<sub>2.5</sub>) measurement campaign in Christchurch, New Zealand

Abstract. MAPM (Mapping Air Pollution eMissions) is a project whose goal is to develop a method to infer airborne particulate matter (PM) emissions maps from in situ PM concentration measurements. In support of MAPM, a winter field campaign was conducted in New Zealand in 2019 (June to September) to obtain the measurements required to test and validate the MAPM methodology. Two different types of instruments measuring PM were deployed: ES-642 remote dust monitors (17 instruments) and Outdoor Dust Information Nodes (ODINs; 50 instruments). The measurement campaign was bracketed by two intercomparisons where all instruments were co-located, with a permanently installed tapered element oscillating membrane (TEOM) instrument, to determine any instrument biases. Changes in biases between the pre- and post-campaign intercomparisons were used to determine instrument drift over the campaign period. Once deployed, each ES-642 was co-located with an ODIN. In addition to the PM measurements, meteorological variables (temperature, pressure, wind speed, and wind direction) were measured at three automatic weather station (AWS) sites established as part of the campaign, with additional data being sourced from 27 further AWSs operated by other agencies. Vertical profile measurements were made with 12 radiosondes during two 24 h periods and complimented measurements made with a mini micropulse lidar and ceilometer. Here we present the data collected during the campaign and discuss the correction of the measurements made by various PM instruments. We find that when compared to measurements made with a simple linear correction, a correction based on environmental conditions improves the quality of measurements retrieved from ODINs but results in over-fitting and increases the uncertainties when applied to the more sophisticated ES-642 instruments. We also compare PM2.5 and PM10 measured by ODINs which, in some cases, allows us to identify PM from natural and anthropogenic sources. The PM data collected during the campaign are publicly available from https://doi.org/10.5281/zenodo.4542559 (Dale et al., 2020b), and the data from other instruments are available from https://doi.org/10.5281/zenodo.4536640 (Dale et al., 2020a).

Air Quality Indexing, Mapping and Principal Components Analysis of Ambient Air Pollutants around Farm Settlements across Ogun State, Nigeria

The focus of this study was to portray the spatial pattern of air quality across seasons in the eight sampled farm settlements using air quality indexes and assess the clusters of monitored air pollutants. The concentrations of air pollutants were determined using in-situ portable gas detectors and particulate counter. The AQI for each criteria pollutants (CO, O3, TSP, PM10, SO2, and PM2.5) was calculated using AQI formulae of the United States Environmental Protection Agency and mapped using the Inverse distance weighting (IDW) interpolation method in the Geographic information systems (GIS) environment. Principal component analysis (PCA) was used to group the parameters and estimate the interrelationships between the loadings of the parameters in each component. The AQI ranges of pollutants which deviated from the acceptable good status are CO (71.98 – 238 and 88.85 – 220.93), NO2 (10.14 – 107.07 and 10.84 – 72.88) and PM2.5 (12.90 – 70.85 and 12.56 – 54.02) for the dry and wet seasons, respectively. There were five and four PCs with eigenvalues > 1, accounting for 69.75% and 61.73% of the total variance during the wet and dry season, respectively. The parameters in each component are as follows; PC1 - TSP, PM10, PM2.5, Bacteria and fungi; PC2 - CO and Temperature; PC3 - relative humidity and O3; PC4 - CO2; PC5 - NO2 and SO2 for the wet season and PC1 - TSP, PM10, PM2.5, Bacteria and fungi; PC2 - NH3 and NO2; PC3 - CO2 and O3; PC4 - Temperature and relative humidity during the dry season. Biomass burning, engine exhausts and fine-particulate related activities are sources of air pollution and such may pose negative implication to human health and environment. Therefore, the use of alternative biomass disposal, regular servicing of processing engines and the wearing of protective wears against dust are recommended.

Using Satellite Data to Map Air Pollution and Improve Health

NASA scientists will be teaming up with epidemiologists in the agency’s first health-focused mission. With satellite data, they’ll find out how air pollution affects health in cities around the world.

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.

High resolution mapping of nighttime light and air pollutants during the COVID-19 lockdown in Wuhan.

The novel coronavirus (COVID-19) has induced unprecedented improvements of air quality due to drastic shrinking of human activities during the pandemic lockdown in 2020. While declines of most air pollutants have been globally evidenced in most cities worldwide, there is few detailed spatial knowledge at local scale. Therefore, we present here a high resolution mapping of the 2018–2020 evolution of human activities and air pollutants in Wuhan. Human activities were assessed by nighttime light radiance. We measured the air quality index (AQI) as the maximum value among air quality sub-indices of SO2, NO2, CO, O3 and particulate matter. We also compared mean monthly pollutant concentration during January–April in 2018, 2019 and 2020. Mapping results show that variations of nighttime light radiance were heterogenous at local scale, showing both rises and declines in the same district. The radiance decreased in eight districts located mostly in the city center, as a result of lower human activity, but the radiance increased in the five surrounding districts, as a consequence of people staying at home. AQI was low during lockdown, averaging at 57, but showed strong daily variations with a slight pollution around February 5 with AQI rising to 126. During this pollution event, particulate matter, SO2, NO2 and CO levels were positively correlated, suggesting common sources, but were not correlated with ozone; and particulate matter, SO2, NO2 and CO decreased with relative humidity, suggesting removal by precipitation. Comparison of 2020 data with previous years shows that particulate matter and NO2 were highly reduced, CO was less reduced due to ongoing power industries, SO2 first declined then increased to exceed 2018–19 values due to coal combustion, and ozone levels was more abundant due both to less NOx pollution and the weekend effect.Supplementary InformationThe online version contains supplementary material available at 10.1007/s10311-021-01222-x.

Atmospheric measurements with the high Spatio-temporal resolution, to be used for air quality at Chhattisgarh state, based on remote sensing and GIS

Remote sensing data for air quality monitoring is important for health research. The advantage of remotely sensed air pollution data includes, but is not limited to, large coverage at a useful spatial and temporal resolution at Chhattisgarh state. Sentinel-5P is a rather new remote sensing data source but requires downloading and computationally intensive processing that is often a barrier to public use. For illustrative purposes, we focus on air pollutants including nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2), Spatio-temporal measurements across mainland Chhattisgarh. The spatial resolution of the measurements allows observations and mapping of air pollution at a scale.

Mapping Gaseous Pollutant Using Quadcopter on Autonomous Waypoint Navigation

Air pollution is a condition in which air quality is damaged and contaminated with substances that are harmful to living things. Along with the increase in the number of motorized vehicles and industrial areas in Indonesia can increase the level of air pollution. Generally, monitoring systems for air pollution are carried out using gas sensors at certain points. However, this method takes a lot of time and cost, as well as low spatial resolution for a wide area. The quadcopter is a type of Unmanned Aerial Vehicle that can move automatically according to the tracking system based on the waypoints of the Global Positioning System. In this study, a quadcopter equipped with a gas sensor is used to map air pollution levels. Mission Planner software is used to determine the waypoints of the quadcopter. The proportional-integral-derivative (PID) control is used to maintain the altitude of the quadcopter while hovering. The gas level can be stored and accessed online on the webserver, in which the results of the gas mapping are displayed on Google Earth. The experiment results show that the measurement of carbon monoxide using the semiconductor gas sensor has an error of 9.6%. The quadcopter can route all the provided waypoints for its navigation. The quadcopter can also maintain the height according to the offered altitude setpoints with a steady-state error of 21.4 % and 15.75%, respectively.

Emerging cross-disciplinary profiles: The Geomatic Epidemiologist

Abstract Issue Geographic information systems (GIS) and remote sensing technologies are increasingly used in Public Health epidemiology, showing a great potential in anticipating and responding to actual and future challenges for the public health system and in improving health services' excellence. According to the evidences collected within a wide meta-research carried on of relevant literature (”GIS geographic information system” and “GIS geographic information system and training” on Pubmed; “epidemiologist use of GIS and training” and “epidemiologist use of gis” on Google Scholar),GIS and new sensing technologies are mostly used to: map air and water pollution, map diseases prevalence, predict infection diseases and vector-spread diseases in big areas, study health service coverage and preparedness in emergencies, map cities and study urban health, study climate changes for decision making. Description of the Problem Specific skills and training are required to address the use of GIS and new sensing technologies.The specific aim of our study is to identify the professional profile of a new figure, called 'Geomatic Epidemiologist' and to define its professional and educational standards, as well as the relevant training programs. Results Data collection and analysis of INAPP and ESCO databases about existing professional profiles (starting from 2016) has allowed drafting a first qualification schema and profile. The profile has been defined according to the 4C model (elaborated by Univaq) distinguishing between Hard Skills (technical knowledge and skills),Soft Skills (cognitive, individual and social) and interpersonal behaviors. Conclusions Profile will be validated with relevant stakeholders and Public Health professionals in order to deepen the understanding of the main competences required to study health issues with GIS and related technologies; to this extent, a questionnaire has been elaborated to evaluate relevance, frequency and complexity of each component of the profile Key messages Developing cross-disciplinary profiles, (i.e. the Geomatic Epidemiologist) integrating clusters of competences (holistic approach). Public health research challenges and excellence.

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models.While, due to the non-linear relationship to reference instruments, fine particulate matter (PM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2.5</sub> ) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ) does not present correlation to reference instrument. As a result, the LCS for CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> is not feasible to be calibrated. Hence, to estimate the CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> concentration,mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities.

Application of a taxi-based mobile atmospheric monitoring system in Cangzhou, China

Increasing air pollution as a result of rapid urbanization is of growing concern worldwide, particularly in populous developing countries. Sparse routine monitoring stations are not sufficient to capture the heterogeneity of air pollution levels in complex urban regions. In this study, a taxi-based mobile atmospheric monitoring system was applied to map the air pollution levels in Cangzhou, China. Fifty taxis equipped with particulate matter (PM) sensors were used to characterize the spatial and temporal variability of PM pollutants. Statistical analyses indicate that the mobile system can provide comparable data to that of fixed monitoring stations and help map the urban air quality. Moreover, the high-resolution air pollution map provided by the mobile monitoring system could address current data gaps, thereby providing valuable information to protect the public health and guide policy design.

Residential Links to Air Pollution and School Children with Asthma in Vilnius (Population Study).

Background and objectives: Many studies have been carried out on the negative health effects of exposure to PM10, PM 2.5, NO2, CO, SO2 and B[a]P for small populations. The main purpose of this study was to explore the association of air pollution to diagnosis of asthma for the whole huge population of school children between 7–17 years in Vilnius (Lithuania) using geographical information system analysis tools. Material and Methods: In the research, a child population of 51,235 individuals was involved. From this large database, we identified children who had asthma diagnosis J45 (ICD-10 AM). Residential pollution concentrations and proximity to roads and green spaces were obtained using the ArcGIS spatial analysis tool from simulated air pollution maps. Multiple stepwise logistic regression was used to explore the relation between air pollution concentration and proximity between the roads and green spaces where children with asthma were living. Further, we explored the interaction between variables. Results: From 51,235 school children aged 7–17 years, 3065 children had asthma in 2017. We investigated significant associations, such as the likelihood of getting sick with age (odds ratio (OR) = 0.949, p < 0.001), gender (OR = 1.357, p = 0.003), NO2 (OR = 1.013, p = 0.019), distance from the green spaces (OR = 1.327, p = 0.013) and interactions of age × gender (OR = 1.024, p = 0.051). The influence of gender on disease is partly explained by different age dependency slopes for boys and girls. Conclusions: According to our results, younger children are more likely to get sick, more cases appended on the lowest age group from 7 to 10 years (almost half cases (49.2%)) and asthma was respectively nearly twice more common in boys (64.1%) than in girls (35.9%). The risk of asthma is related to a higher concentration of NO2 and residence proximity to green spaces.

Do Environmental Factors Predict Changes To Physical Activity And Sedentary Behaviour Equally?

PURPOSE: To investigate environmental determinants of change to sedentary behaviour (SB), light-intensity physical activity (LPA) and moderate-to-vigorous intensity physical activity (MVPA) in those at risk of diabetes. METHODS: Cohort of 808 adults recruited from family practice (age = 63 years, BMI = 32 kg/m2, women = 36%) followed up annually over 3 years. Participants were included based on being above the 90th percentile of the Leicester Diabetes Risk Score, indicating a high risk. SB, LPA and MVPA were measured by accelerometer (Actigraph GT3X) using the Freedson cut-points with at least 4 valid days of data. Geographical and air pollution mapping were used to link environmental data to participant’s home address. Data were analysed using a repeated measures design assessing determinants of change to SB, LPA and MVPA. Results are presented as mean (SD) or mean (95% CI). RESULTS: At baseline, accelerometers were worn for an average of 649 (83) minutes/day, during which an average of 27 (25) minutes/day, 288 (77) minutes/day and 543 (100) minutes/day were spent in MVPA, LPA and SB respectively. Across the study period, wear time did not change, whilst SB increased by 9 (7, 10) minutes /day per year, matched by a decrease in LPA and MVPA of 7 (5, 8) and 2 (1, 2) minutes/day per year respectively. Less green space, higher road connectivity (busier traffic), greater air pollution and a higher density of fast food restaurants were all determinants of increasing SB and decreasing LPA (See Table 1), but none were associated with change to MVPA. Environmental factors were stronger determinants of change to SB and LPA than an overall index of social deprivation. CONCLUSION: In this high risk population, a less healthy environment predicted greater annual increases in SB and decreases in LPA, but not MVPA. Policies and interventions aimed at improving the liveable environment may therefore act to slow the trajectory of increasing SB and decreasing LPA in populations at risk of diabetes.Table 1: Association between environmental factors and annual change to SB, LPA and MVPA.Data as mean (95% CI). Adjusted for age, sex and ethnicity. Bold indicates significance (p < 0.05).

Graph-Deep-Learning-Based Inference of Fine-Grained Air Quality From Mobile IoT Sensors

Internet-of-Things (IoT) technologies incorporate a large number of different sensing devices and communication technologies to collect a large amount of data for various applications. Smart cities employ IoT infrastructures to build services useful for the administration of the city and the citizens. In this article, we present an IoT pipeline for acquisition, processing, and visualization of air pollution data over the city of Antwerp, Belgium. Our system employs IoT devices mounted on vehicles as well as static reference stations to measure a variety of city parameters, such as humidity, temperature, and air pollution. Mobile measurements cover a larger area compared to static stations; however, there is a tradeoff between temporal and spatial resolution. We address this problem as a matrix completion on graphs problem and rely on variational graph autoencoders to propose a deep learning solution for the estimation of the unknown air pollution values. Our model is extended to capture the correlation among different air pollutants, leading to improved estimation. We conduct experiments at different spatial and temporal resolution and compare with state-of-the-art methods to show the efficiency of our approach. The observed and estimated air pollution values can be accessed by interested users through a Web visualization tool designed to provide an air pollution map of the city of Antwerp.

Uncertainty in collocated mobile measurements of air quality.

Mobile mapping of air pollution has the potential to provide pollutant concentration data at unprecedented spatial scales. Characterizing instrument performance in the mobile context is challenging, but necessary to analyze and interpret the resulting data. We used robust statistical methods to assess mobile platform performance using data collected with the Aclima Inc. mobile air pollution measurement and data acquisition platform installed on three Google Street View cars. They were driven throughout the greater Denver metropolitan area between July 25, 2014 and August 14, 2014, measuring ozone (O3), nitrogen dioxide (NO2), nitric oxide (NO), black carbon (BC), and size-resolve particle number counts (PN) between 0.3 μm and 5.0 μm diameter. August 6, 2014 was dedicated to parked and moving collocations among the three cars, allowing an assessment of measurement precision and bias. We used the median absolute deviation (MAD) to estimate instrument precision from outdoor, parked collocations. Bias was assessed by measurements obtained from parked cars using the standard deviation of median values over a collocated measurement period, as well as by Passing-Bablok regression statistics while the cars were moving and collocated. For the moving collocation periods, we compared the distribution of 1-σ standard deviations among the 3 cars to the estimated distribution assuming only measurement uncertainty (precision and bias). The distribution of mobile measurements agreed well with the theoretical uncertainty distribution at the lower end of the distribution for O3, NO2, and PN. We assert that the difference between the actual and theoretical distributions is due to real spatial variability between pollutants. The agreement between the parked car estimates of uncertainty and that measured during the mobile collocations (at the lower quantiles) provides evidence that on-road collocation while parked could be sufficient for estimating measurement uncertainties of a mobile platform, even when extended to the moving environment.

JUSense: A Unified Framework for Participatory-based Urban Sensing System

Participatory sensing has become an effective way of sensing urban dynamics due to the widespread availability of smartphones among citizens. Traditionally, separate urban sensing applications are designed to monitor different urban dynamics like environment, transportation, mobility, etc. However, combining these applications to aggregate information can lead to various new inferences. The main objective of this work is to improve urban sensing applications by overcoming their individual limitations. A unified framework called JUSense (Judicious Urban Sensing) is proposed that can derive benefits from these applications by combining their functionalities. JUSense provides the opportunity for applications to tackle the challenges associated with data collection, aggregation of data in cloud, calibration, data cleaning, and prediction. A multi-view fusion model is proposed for spatiotemporal urban air and noise pollution map generation. Further, a random forest classifier is built to classify the driving events. Here, large scale experiments are performed to evaluate the efficacy of JUSense on real-world dataset. Both the fusion model and the random forest classifier yield better accuracies compared to the baseline methods. Additionally, case studies are conducted to show the advantages that can arise out of the mutual interactions among the applications.

Low-Cost Data Acquisition Station for Microclimate Detection and Public Health Risks Management: A Case Study of Casablanca

the environmental data acquisition station is used to manage the public health and detection of a microclimate of a city for a broadcast processed high precision data for users. This research article is summarizing the applications of Electronics and GIS applications to health risks management due to air pollution, noise and microclimate change detection, to study the geography of urban health risks and environmental change in Casablanca, Morocco. acquisition of environmental and Climatological Data using sensor networks, results a new possibilities and advantages, of environmental data monitoring using electronic circuit with a broadcasting system via a technology with wireless transmitter. The main object of this research is to present a simple and rapid method to provide instant microclimate and environment data, which can be used to manage the public risks caused by pollution and environment changes. As the climate and environment conditions are variable from a place to place, it is difficult to get accurate climate and environment for a particular location in a city. With the advancement of technology, especially data acquisition systems, the problem of large set up area and cost has been reduced. The data acquisition station can be set up at any place and provide an accurate and instant climate and environment report to any citizen of the city. The report of the data acquisition system can be used by the users to choose the best place for them to live due to their diseases. In addition, these data can be collected in a GIS system to create a microclimate change and air pollution map of different zones in the city, predict the future climate, and air pollution of the city.

Spatial Mapping of a Highly Non-Uniform Distribution of Particle-Bound PAH in a Densely Populated Urban Area

In this work, a 2-D gridded air pollution map with a high resolution of 50 × 50 m2 was proposed to help the exposure assessment studies focusing on the association between air pollutants and their health effects. To establish a reliable air pollution map in a 2 × 2 km2 urban area, a mobile monitoring procedure and a data process were developed. Among the various vehicle-related air pollutants, the particle-bound polycyclic aromatic hydrocarbon (pPAH) was chosen as a sensitive indicator. The average pPAH concentration on major roads (293.1 ng/m3) was found to be 35 times higher than that at a background location (8.4 ng/m3). Based on the cell-based pPAH concentrations, the 50 × 50 m2 cells in the air pollution map were categorized into five pollution levels. The higher air pollution levels were generally shown by the cells close to the major traffic emission points. The proposed map can be used to make various policies regarding land use and traffic flow control in urban areas. Estimation of the personal exposure level to air pollutants is possible at a reliable location using the highly resolved 2-D gridded air pollution map in exposure assessment studies.