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Measuring TrafficRelated Air Pollution Using Smart Sensors In Sri Lanka: Before And During A New Traffic Plan
Abstract
Motor vehicle emissions are the primary air pollution source in cities worldwide. Changes in traffic flow in a city can drastically change overall levels of air pollution. The level of air pollution may vary significantly in some street segments compared to others, and a small number of stationary ambient air pollution monitors may not capture this variation. This study aimed to evaluate air pollution before and during a new traffic plan established in March 2019 in the city of Kandy, Sri Lanka, using smart sensor technology. Street level air pollution data (PM2.5 and NO2 ) was acquired using a mobile air quality sensor unit before and during the implementation of the new traffic plan. The sensor unit was mounted on a police traffic motorcycle that travelled through the city four times per day. Air pollution in selected road segments was compared before and during the new traffic plan, and the trends at different times of the day were compared using data from a stationary smart sensor. Both PM2.5 and NO2 levels were well above the World Health Organization (WHO) 24-hour guidelines during the monitoring period, regardless of the traffic plan period. Most of the road segments had comparatively higher air pollution levels during compared to before the new traffic plan. For any given time (morning, midday, afternoon, evening), day of the week, and period (before or during the new traffic plan), the highest PM2.5 and NO2 concentrations were observed at the road segment from Girls High School to Kandy Railway Station. The mobile air pollution monitoring data provided evidence that the mean concentration of PM2.5 during the new traffic plan (116.7 µg m-3) was significantly higher than before the new traffic plan (92.3 µg m-3) (p < 0.007). Increasing spatial coverage can provide much better information on human exposure to air pollutants, which is essential to control traffic related air pollution. Before implementing a new traffic plan, careful planning and improvement of road network infrastructure could reduce air pollution in urban areas.
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Evidence of associations between exposure to ambient air pollution and health outcomes are sparse in the South Asian region due to limited air pollution exposure and quality health data. This study investigated the potential impacts of ambient particulate matter (PM) on respiratory disease hospitalization in Kandy, Sri Lanka for the year 2019. The Generalized Additive Model (GAM) was applied to estimate the short-term effect of ambient PM on respiratory disease hospitalization. As the second analysis, respiratory disease hospitalizations during two distinct air pollution periods were analyzed. Each 10 μg/m3 increase in same-day exposure to PM2.5 and PM10 was associated with an increased risk of respiratory disease hospitalization by 1.95% (0.25, 3.67) and 1.63% (0.16, 3.12), respectively. The effect of PM2.5 or PM10 on asthma hospitalizations were 4.67% (1.23, 8.23) and 4.04% (1.06, 7.11), respectively (p < 0.05). The 65+ years age group had a higher risk associated with PM2.5 and PM10 exposure and hospital admissions for all respiratory diseases on the same day (2.74% and 2.28%, respectively). Compared to the lower ambient air pollution period, higher increased hospital admissions were observed among those aged above 65 years, males, and COPD and pneumonia hospital admissions during the high ambient air pollution period. Active efforts are crucial to improve ambient air quality in this region to reduce the health effects.
The COVID-19 pandemic has led to the complete lockdown of many cities worldwide. Lockdowns have significantly changed human behaviour, such as fewer or no industrial activities and limited road and air transport, affecting the environment. In this study, we aimed to assess the variability and trends of PM2.5 (mass concentration of airborne particulate matter < 2.5μm) and carbon monoxide (CO) before and during the COVID-19 lockdown period in Sri Lanka. Data were collected in “Urban Background”, “Public & Mixed Residential”, and “Primary Residential” areas using small sensor technology, "KOALA" air quality sensor units, from five locations, three in Kandy, and two in Colombo city. Daily averages (24h) and daytime averages (08:00 AM to 8:00 PM) were calculated for the period before (before March 20th) and during (March 20th to May 10th) the lockdown. Air pollutions level before & during COVID-19 lockdown were compared, then Mann-Kendall and Sen's slope tests were performed to determine trends of PM2.5 and CO, and the magnitude of the trend. Meteorological parameters were fairly similar before and during both cities' lockdown periods, while both PM2.5 and CO concentrations declined. The highest average reductions of PM2.5 and CO were observed in Colombo's “Urban Background” area (52.4% and 46.7%, respectively). In Kandy, “Urban Background” site had a higher reduction of PM2.5 and CO (30.2% and 41.2%, respectively), compared to “Primary Residential” (10% and 9%, respectively). The daily averages of the pollutants' concentrations were higher before the lockdown period compared to during. Overall, a significant downward trend was observed of air pollutants over the entire study period. In Sri Lanka, the COVID-19 lockdown improved air quality significantly in urban areas.
In this paper, hourly observations of precipitation, wind, and PM2.5 and PM10 concentrations in Qinhuangdao from 2016 to 2018 were used to study the effects of precipitation and wind on PM2.5 and PM10 concentrations. The results show that precipitation has a certain wet scavenging effect on PM2.5 and PM10, and the scavenging effect on PM10 is greater than that on PM2.5. Precipitation above moderate rainfall is concentrated from May to September, and light rain in winter increases the concentration of pollutants. The changes of PM2.5 before and after precipitation are related to the initial concentration of PM2.5 before precipitation, precipitation intensity, and precipitation duration. The scavenging effect of precipitation on PM10 is closely related to the initial concentration of PM10 before precipitation. The higher the initial concentration of PM10 is, the greater the removal amount of precipitation will be. Moderate or above pollution weather mainly occurs in the northeast, southwest, and west wind meteorological conditions; the more westerly the wind, the more the pollution; north wind and northwest wind have the most obvious scavenging effect on PM2.5 and Pm10; when the wind speed increases to 2 m/s, the concentration of PM2.5 and PM10 can be reduced; when the wind speed is more than 4 m/s, the concentration of PM10 increases under the south wind, southeast wind, east wind, and northeast wind.
Traffic-induced air pollutant emissions are currently rising rapidly. However, measurement of the roadside environment and calculation of the emission factors for traffic-induced PM2.5 are restricted to certain locations and periods due to the limitations of conventional air monitoring techniques. This paper introduces a portable sensor package with a laser light-scattering PM2.5 sensor and an electrochemical CO sensor to measure roadside PM2.5 and CO concentrations. The low-cost sensor package underwent local calibration using reference instruments at the Chinese Research Academy of Environmental Sciences (CRAES). The results showed a high level of correlation (r in the range of 0.94–0.95 and 0.81–0.83 for PM2.5 and CO, respectively) between measurements using the sensor packages and those measured by the reference equipment. The study found that the low-cost sensor packages were able to deliver reliable measurements of PM2.5 and CO concentrations. Four low-cost sensor packages were deployed along a short section of an expressway to measure roadside PM2.5 and CO concentrations. The directly measured concentrations were firstly calibrated with the temperature and humidity. The corrected PM2.5 concentrations from each side of the road were different, while the corrected CO concentrations were similar on both sides of the road. Therefore, only the PM2.5 measurements were applied in this study’s box model. The assumption of perfect mixing in order for the box model to be applied was shown by the results to be valid to some extent. The PM2.5 emission factors for opposite sides of the road should be calculated separately based on the direction of traffic flow. The PM2.5 emission factors calculated in this study were variable, being impacted by traffic conditions and meteorological conditions. The paper presents a method for obtaining PM2.5 emission factors based on a box model. This method is a promising way of monitoring air pollution in the roadside environment.
Recent studies have demonstrated that mobile sampling can improve the spatial granularity of land use regression (LUR) models. Mobile sampling campaigns deploying low-cost (<$300) air quality sensors could potentially offer an inexpensive and practical approach to measure and model air pollution concentration levels. In this study, we developed LUR models for street-level fine particulate matter (PM2.5) concentration levels in Seoul, South Korea. 169 h of data were collected from an approximately three week long campaign across five routes by ten volunteers sharing seven AirBeams, a low-cost ($250 per unit), smartphone-based particle counter, while geospatial data were extracted from OpenStreetMap, an open-source and crowd-generated geographical dataset. We applied and compared three statistical approaches in constructing the LUR models - linear regression (LR), random forest (RF), and stacked ensemble (SE) combining multiple machine learning algorithms - which resulted in cross-validation R2 values of 0.63, 0.73, and 0.80, respectively, and identification of several pollution 'hotspots.' The high R2 values suggest that study designs employing mobile sampling in conjunction with multiple low-cost air quality monitors could be applied to characterize urban street-level air quality with high spatial resolution, and that machine learning models could further improve model performance. Given this study design's cost-effectiveness and ease of implementation, similar approaches may be especially suitable for citizen science and community-based endeavors, or in regions bereft of air quality data and preexisting air monitoring networks, such as developing countries.
Background:
Traffic-related air pollution (TRAP) is one of the major sources of exposure in urban areas and has been associated with a wide range of adverse human health effects. Much of the Canadian population is regularly exposed to TRAP as a result of daily activities (e.g., commuting) and a significant portion of the population resides in close proximity to major roadways. The objective of this scoping review is to develop an evidence map of the epidemiological literature of the human health effects of exposure to TRAP, to support future reviews and assessments by Health Canada.
Methods:
Literature searches will be conducted in Ovid EMBASE and Ovid MEDLINE database. DistillerSR will be used to manage the review process. Two reviewers will independently screen the studies in a two-part process (title and abstract; full text) for eligibility. Epidemiological studies and reviews will be included if they report on the human health effects of exposure to TRAP. Data collection will include study design parameters and human health outcomes evaluated in the study. A descriptive analysis will be used to provide a high-level summary of the number of studies evaluating the different types of health effects and cross-tabulations by study design parameters.
Discussion:
The scoping review will be used to identify subject areas for more detailed review and evaluation of the human health effects of TRAP by the Air Health Effects Assessment Division of Health Canada.
This paper presents an experimental study on real-time air pollution monitoring using wireless sensors on public transport vehicles. The study is part of the GreenIoT project in Sweden, which utilizes Internet-of-Things to measure air pollution level in the city center of Uppsala. Through deploying low-cost wireless sensors, it is possible to obtain more fine-grained and real-time air pollution levels at different locations. The sensors on public transport vehicles complement the readings from stationary sensors and the only ground level monitoring station in Uppsala. The paper describes the deployment of wireless sensors on Uppsala buses and the integration of the mobile sensor network with the GreenIoT testbed. Extensive experiments have been conducted to evaluate the communication quality and data quality of the system.
An online GPRS-Sensors Array for air pollution monitoring has been designed, implemented, and tested. The proposed system consists of a Mobile Data-Acquisition Unit (Mobile-DAQ) and a fixed Internet-Enabled Pollution Monitoring Server (Pollution-Server). The Mobile-DAQ unit integrates a single-chip microcontroller, air pollution sensors array, a General Packet Radio Service Modem (GPRS-Modem), and a Global Positioning System Module (GPS-Module). The Pollution-Server is a high-end personal computer application server with Internet connectivity. The Mobile-DAQ unit gathers air pollutants levels (CO, NO2, and SO2), and packs them in a frame with the GPS physical location, time, and date. The frame is subsequently uploaded to the GPRS-Modem and transmitted to the Pollution-Server via the public mobile network. A database server is attached to the Pollution-Server for storing the pollutants level for further usage by various clients such as environment protection agencies, vehicles registration authorities, and tourist and insurance companies. The Pollution-Server is interfaced to Google Maps to display real-time pollutants levels and locations in large metropolitan areas. The system was successfully tested in the city of Sharjah, UAE. The system reports real-time pollutants level and their location on a 24-h/7-day basis.
Low-cost air quality sensors are increasingly being used in many applications; however, many of their performance characteristics have not been adequately investigated. This study was conducted over a period of 13 months using low-cost air quality monitors, each comprising two low-cost sensors, which were subjected to a wide range of pollution sources and concentrations, relative humidity and temperature at four locations in Australia and China. The aim of the study was to establish the performance characteristics of the two low-cost sensors (a Plantower PMS1003 for PM2.5 and an Alphasense CO–B4 for carbon monoxide, CO) and the KOALA monitor as a whole under various conditions. Parameters evaluated included the inter-variability between individual monitors, the accuracy of monitors in comparison with the reference instruments, the effect of temperature and RH on performance of the monitors, the responses of the PM2.5 sensors to different types of aerosols, and the long-term stability of the PM2.5 and CO sensors. The monitors showed high inter-correlations (r > 0.91) for both PM2.5 and CO measurements. The monitor performance varied with location, with moderate to good correlations with reference instruments for PM2.5 (0.44<R² < 0.91) and CO (0.37<R² < 0.90). The monitors performed well at relative humidity <75% and high temperature conditions; however, two monitors in Beijing failed at low temperatures, probably due to electronic board failure. The PM2.5 sensor was less sensitive to marine aerosols and fresh vehicle emissions than to mixed urban background emissions, aged traffic emissions and industrial emissions. The long-term stability of the PM2.5 and CO sensors was good, while CO relative errors were affected by both high and low temperatures. Overall, the KOALA monitors performed well in the environments in which they were operated and provided a valuable contribution to long-term air quality monitoring within the elucidated limitations.
Kandy is the second largest city in Sri Lanka and a major tourist destination. It is a fast growing city with continuous construction of buildings, roads and historical places. More than 100 samples of fine particulate matter (PM) were collected using a GENT stacked filter sampler from a fixed site at the regional sampling station of Department of Meteorology situated in Katugastota, Kandy over the period of 2012 to 2014. Black carbon (BC) in these filters were determined by reflectance measurements while their elemental compositions were determined using the X-ray fluorescence spectrometry. Analysis of the elemental data suggests that the PM in Kandy originates largely from re-suspended soil and anthropogenic sources. The fine particulate matter data including BC and major elements (Na, Mg, Al, Si, Cl, Fe, Zn, Ni, Cu, V, S, Br, Pb, Cr, K, Ca and Ti) was analyzed using EPA-PMF version 5.0 (Positive Matrix Factorization) to explore the possible sources of the PM at the study site. Five factors were found and identified as soil, aged sea salt, vehicular emissions, biomass burning, and industrial sources.
The quality of air is a major concern in modern cities as pollutants have been demonstrated to have significant impact on human health. Networks of fixed monitoring stations have been deployed in urban areas to provide authorities with data to define and enforce dynamically policies to reduce pollutants, for instance by issuing traffic regulation measures. However, fixed networks require careful placement of monitoring stations to be effective. Moreover, changes in urban arrangement, activities, or regulations may affect considerably the monitoring model, especially when budget constraints prevent from relocating stations or adding new ones to the network. In this chapter we discuss a different approach to environmental monitoring through mobile monitoring devices implementing a Vehicular Sensor Network (VSN) to be deployed on the public transport bus fleet of Palermo.
Traditionally, pollution measurements are performed using expensive equipment at fixed locations or dedicated mobile equipment laboratories. This is a coarse-grained and expensive approach where the pollution measurements are few and far in-between. In this paper, we present a vehicular-based mobile approach for measuring fine-grained air quality in real-time. We propose two cost effective data farming models -- one that can be deployed on public transportation and the second a personal sensing device. We present preliminary prototypes and discuss implementation challenges and early experiments.
In this paper, the use of wireless vehicular sensor networks (VSN) were explored for the purpose of environmental monitoring. A prototype for the mobile sensor node that can be mounted onto vehicles, such as public buses, was introduced and from the experiments performed, it was demonstrated that the use of mobile vehicular sensors instead of static sensors allowed for higher spatial coverage at the expense of lower temporal resolution.
Micro-climate monitoring usually requires deploying a large number of measurement tools. By adopting vehicular wireless sensor networks (VSNs), we can use fewer tools to achieve fine-grained monitoring. This work proposes a VSN architecture to realize micro-climate monitoring based on GSM short messages and availability of GPS receivers on vehicles. We demonstrate our prototype of a ZigBee-based car network to monitor the concentration of carbon dioxide (CO<sub>2</sub>) gas in areas of interest. The reported data are sent to a server, which is integrated with Google Maps as our user interface. Since mobility of these vehicles is not controllable and sending short messages incurs charges, we also design an on-demand approach to adjust vehicles' reporting rates to balance between the micro-climate accuracy and the communication cost.
On 17th February 2003, a congestion charging scheme (CCS), operating Monday–Friday, 07:00–18:00, was introduced in central London along with a programme of traffic management measures. We investigated the potential impact of the introduction of the CCS on measured pollutant concentrations (oxides of nitrogen (NOX, NO and NO2), particles with a median diameter less than 10 microns (PM10), carbon monoxide (CO) and ozone (O3)) measured at roadside and background monitoring sites across Greater London. Temporal changes in pollution concentrations within the congestion charging zone were compared to changes, over the same time period, at monitors unlikely to be affected by the CCS (the control zone) and in the boundary zone between the two. Similar analyses were done for CCS hours during weekends (when the CCS was not operating).Based on the single roadside monitor with the CCS Zone, it was not possible to identify any relative changes in pollution concentrations associated with the introduction of the scheme. However, using background monitors, there was good evidence for a decrease in NO and increases in NO2 and O3 relative to the control zone. There was little change in background concentrations of NOX. There was also evidence of relative reductions in PM10 and CO. Similar changes were observed during the same hours in weekends when the scheme was not operating.The causal attribution of these changes to the CCS per se is not appropriate since the scheme was introduced concurrently with other traffic and emissions interventions which might have had a more concentrated effect in central London. This study provides important pointers for study design and data requirements for the evaluation of similar schemes in terms of air quality. It also shows that results may be unexpected and that the overall effect on toxicity may not be entirely favourable.
To estimate the relationship between air pollution and lung cancer, a nested case-control study was set up within EPIC (European Prospective Investigation on Cancer and Nutrition). Cases had newly diagnosed lung cancer, accrued after a median follow-up of 7 years among the EPIC ex-smokers (since at least 10 years) and never smokers. Three controls per case were matched. Matching criteria were gender, age (+/-5 years), smoking status, country of recruitment and time elapsed between recruitment and diagnosis. We studied residence in proximity of heavy traffic roads as an indicator of exposure to air pollution. In addition, exposure to air pollutants (NO(2), PM10, SO(2)) was assessed using concentration data from monitoring stations in routine air quality monitoring networks. Cotinine was measured in plasma. We found a nonsignificant association between lung cancer and residence nearby heavy traffic roads (odds ratio = 1.46, 95% confidence interval, CI, 0.89-2.40). Exposure data for single pollutants were available for 197 cases and 556 matched controls. For NO(2) we found an odds ratio of 1.14 (95% CI, 0.78-1.67) for each increment of 10 microg/m(3), and an odds ratio of 1.30 (1.02-1.66) for concentrations greater than 30 microg/m(3). The association with NO(2) did not change after adjustment by cotinine and additional potential confounders, including occupational exposures. No clear association was found with other pollutants.
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