Ambient Air Quality Monitoring Network Research Articles

A hybrid approach for integrating micro-satellite images and sensors network-based ground measurements using deep learning for high-resolution prediction of fine particulate matter (PM2.5) over an indian city, lucknow

The detrimental impacts of fine particulate matter (PM2.5) on human health, climate, ecosystems, crops, and building materials are well-established. However, there remain unresolved inquiries regarding the precise location of the sources of PM2.5. This study is the first attempt to use a calibrated sensors-based ambient air quality monitoring network (SAAQM network) and regulatory government monitors to train micro-satellite images for high spatial-resolution air pollution field determination of PM2.5 in Lucknow, Uttar Pradesh, India. A hybrid approach is developed to integrate three different datasets that include microsatellite images, PM2.5 ground measurements, and supporting information (meteorological parameters and geographical coordinates), to be fed into a Random Trees-Random Forest- Convolutional Neural Network (RT-RF-CNN) joint model to estimate PM2.5 concentrations at a sub-km level. The RT-RF-CNN joint model can derive PM2.5 concentrations at a spatial resolution of 500 m with statistically significant indicators such as spatial r of 0.9, a low root-mean-square error of 26.9 μg/m3 and a mean absolute error of 17.2 μg/m3. Based on our approach, the PM2.5 prediction maps using micro-satellite images (spatial resolution of 3m/pixel) and RT-RF-CNN joint model were generated for each day throughout the study period (December 2021–December 2022). The inter-grid comparison of these maps revealed the intra-urban local hotspots and coolspots at a fine-granular level seasonally, monthly, and daily. It is observed that the monsoon season has the highest number of coolspots (67%), while winter (0.1%), post-monsoon (0.5%) and summer (11%) have fewer. It is noted that the high temporal-spatial information of PM2.5 estimates from our integrated approach is not achievable by ground-based measurements and other existing satellite-based estimates alone. The findings of this study have potential applications on a diverse array, encompassing near real-time daily PM2.5 predicted maps, specific air pollution hotspot identification, PM2.5 exposure assessment at the neighbourhood level, and integration of remote sensing-based micro-satellite images and ground-based measurements.

Quantitative analysis of air pollution levels and its health implications in Delhi-NCR: A longitudinal study

Air pollution is a pressing global issue, particularly acute in rapidly urbanizing regions like Delhi-NCR. This research presents a longitudinal study to quantitatively analyze air pollution levels and their health implications in Delhi-NCR. The research objectives include assessing air quality trends, examining the relationships between various pollutants, and investigating the impact of air pollution on respiratory admissions. Data was sourced from the Ambient Air Quality Monitoring Network, covering the years 2020 to 2022, focusing on key pollutants, including PM2.5, PM10, NO2, SO2, CO, and O3. A range of data analysis techniques, including correlation analysis, regression analysis, longitudinal data analysis, spatial analysis, and compliance with WHO guidelines, were applied. Key findings highlight persistently high levels of air pollutants, strong interrelations among pollutants, and consistent respiratory admissions, especially in winter. Regression analysis confirms a significant link between PM2.5 and respiratory admissions. Longitudinal analysis demonstrates the long-term health consequences of air pollution, and spatial analysis reveals district-wise variations in air quality. The study also emphasizes seasonal strategies, compliance with international guidelines, and socioeconomic disparities in health outcomes. This research has broader implications for evidence-based policymaking, emphasizing the urgency of addressing air quality concerns. It calls for targeted interventions, district-level strategies, season-specific measures, and equitable access to healthcare. These findings offer a comprehensive basis for addressing the air quality crisis in Delhi-NCR, ultimately promoting the well-being of its residents.

Characteristics and mechanism of a persistent ozone pollution event in Pearl River Delta induced by typhoon and subtropical high

Ozone (O3) is a significant air pollutant in the Pearl River Delta (PRD) region, with severe pollution events primarily associated with the Western Pacific Subtropical High (WPSH) and typhoon periphery synoptic weather patterns. To investigate the continuity and differences in O3 pollution processes influenced by these patterns, we analyze a severe O3 pollution event in Zhuhai from September 10 to 13, 2022. The study collects vertical profiles of O3 from the differential absorption O3 lidar and wind fields observed by the Doppler wind profile lidar. We utilize ground-level monitoring data from national ambient air quality monitoring network and meteorological stations to analyze diurnal variations of air pollutant concentrations and ground-level meteorological conditions. The study reveals that both the WPSH and the typhoon periphery create favorable meteorological conditions for severe O3 pollution events. The highest temperatures during the WPSH and typhoon periphery stages are 32.1 °C and 37.5 °C, respectively. During the WPSH control stage, the combined effects of the inversion layer and light static wind hinder O3 dispersion. The ground-level O3 concentration reaches a maximum of 106 ppb. Then the typhoon periphery control stage, the ground-level O3 peak increases to 170 ppb, showing a 60% increase compared to the WPSH stage. O3 horizontal and vertical transport processes play a crucial role in the O3 episode during this stage. The northerly winds predominantly transport pollutants from upstream urban clusters to the local area, and intense downdrafts facilitate the downward transport of O3 from the residual layer. The study investigates the mechanisms of three-dimensional O3 transport within the boundary layer influenced by the WPSH and typhoon periphery in the PRD region. The results contribute valuable insights to pollution events in the PRD region under typical synoptic weather patterns.

Smart solutions for urban health risk assessment: A PM2.5 monitoring system incorporating spatiotemporal long-short term graph convolutional network

Current spatial-temporal early warning systems aim to predict outdoor air quality in urban areas either at short or long temporal horizons. These systems implemented architectures without considering the geographical distribution of each air quality monitoring station, increasing the uncertainty of the forecasting framework. This study developed an integrated spatiotemporal forecasting architecture incorporating an extensive air quality PM2.5 monitoring network and simultaneously forecasts PM2.5 concentrations at all locations, allowing the monitoring of the health risk associated with exposure to these levels. First, this study uses a graph convolutional layer to incorporate the spatial relationship of the neighboring stations at their current state with real-time measurements. Then, it is coupled to a deep learning temporal model to form the long- and short-term time-series graph convolutional network (LSTGraphNet) model, anticipating high pollutant concentration events. This work tested the proposed model with a case study of an existing ambient air quality monitoring network in South Korea. LSTGraphNet model showed prediction performances of PM2.5 at multiple monitoring stations with a mean absolute error (MAE) of 1.82 μg/m3, 4.46 μg/m3, and 4.87 μg/m3 for forecasting horizons of one, three, and 6 h ahead, respectively. Compared to conventional sequential models, this architecture was superior among the state-of-the-art baselines, where the MAE decreased to 41%, respectively. The results of the study showed that the proposed architecture was superior to conventional sequential models and could be used as a tool for decision-making in smart cities by revealing hotspots of higher and lower PM2.5 concentrations in the long term.

Analysis of Air Pollution Data in India between 2015 and 2019

India suffers from among the worst air pollution in the world. In response, a large government effort to increase air quality monitoring is underway. We present the first comprehensive analysis of government air quality observations from 2015–2019 for PM10, PM2.5, SO2, NO2 and O3 from the Central Pollution Control Board (CPCB) Continuous Ambient Air Quality Monitoring (CAAQM) network and the manual National Air Quality Monitoring Program (NAMP), as well as PM2.5 from the US Air-Now network. We address inconsistencies and data gaps in datasets using a rigorous procedure to ensure data representativeness. We find particulate pollution dominates the pollution mix across India with virtually all sites in northern India (divided at 23.5°N) exceeding the annual average PM10 and PM2.5 residential national ambient air quality standards (NAAQS) by 150% and 100% respectively, and in southern India exceeding the PM10 standard by 50% and the PM2.5 standard by 40%. Annual average SO2, NO2 and MDA8 O3 generally meet the residential NAAQS across India. Northern India has (~10%–130%) higher concentrations of all pollutants than southern India, with only SO2 having similar concentrations. Although inter-annual variability exists, we found no significant trend of these pollutants over the five-year period. In the five cities with Air-Now PM2.5 measurements - Delhi, Kolkata, Mumbai, Hyderabad and Chennai, there is reasonable agreement with CPCB data. The PM2.5 CPCB CAAQM data compares well with satellite derived annual surface PM2.5 concentrations (Hammer et al., 2020), with the exception of the western desert region prior to 2018 when surface measurements exceeded satellite retrievals. Our reanalyzed dataset is useful for evaluation of Indian air quality from satellite data, atmospheric models, and low-cost sensors. Our dataset also provides a baseline to evaluate the future success of National Clean Air Programme as well as aids in assessment of existing and future air pollution mitigation policies.

Comparing types of exposure metrics for identifying the effects of air pollution: an application in a panel of COPD patients

BACKGROUND AND AIM: Measured or modelled concentrations used in epidemiological studies can differ from true exposures, a difference known as measurement error. To account for this, personal exposure measurements can be regarded as the “gold-standard” method but do not differentiate between the effects of indoor- and outdoor-generated pollution. We apply a statistical apportionment method to identify and separate the effects of personal exposure to air pollution from indoor and outdoor sources on chronic obstructive pulmonary disease (COPD) patients in London. METHODS: A panel of 130 patients was followed up for an average period of 128 days during which each patient carried sensors measuring PM2.5, PM10, NO2, NO, CO and O3 at one-minute resolution. Total personal exposures (PExT) were separated into those from indoor- (PExI) and outdoor-generated pollution (PExO) using GPS data and matching ambient concentrations from the London reference ambient air quality monitoring network. Each patient recorded daily respiratory symptoms, peak expiratory flow (PEF) and exacerbation of symptoms. The associations of these outcomes with PExT, PExI, PExO and ambient pollution were assessed with mixed-effects models. RESULTS:Preliminary results for PM2.5 have shown that while total personal exposure was not associated with any outcome, a unit increase in PExO was found to increase the odds of exacerbation, cough and sputum by approximately 1.0%. Using ambient concentrations, which is the equivalent error-prone proxy to PExO, these odds were decreased to 0.6-0.7% for exacerbation and cough, while for sputum the finding was not statistically significant. PEF was negatively associated only with PExI. Work is ongoing for the other pollutants. CONCLUSIONS:Our findings suggest that the exposure metric used in identifying the health effects of air pollution is important in epidemiological studies. Using proxies can introduce measurement error bias. For decision-making, it is important to separate the effects of indoor- and outdoor-generated pollution as different policies apply to each. KEYWORDS: Air pollution, Respiratory outcomes, Methodological study design, Exposure assessment, Measurement error

Does national air quality monitoring reduce local air pollution? The case of PM2.5 for China

Fine particulate matter (PM2.5) has become a major pressing challenge for China and remains a concern of its central government. This paper draws on a natural experiment generated by the National Ambient Air Quality Monitoring Network (NAAQMN) program in China to explore whether national air quality monitoring reduces local air pollution. In this study, we use a city-level dataset for 4200 Chinese cities covering 2001–2015 and a difference-in-differences (DID) assessment design to assess the impact of the NAAQMN program on local PM2.5 emissions in China. The results suggest that the NAAQMN program significantly reduces the local PM2.5 concentrations by 1.325 mg/m3, and each additional NAAQMN program will cause a decrease of 0.154 mg/m3 in the local PM2.5 concentrations. Furthermore, we determine the heterogeneous impacts of the NAAQMN program on local PM2.5 emission levels through the local government leaders’ characteristics, PM2.5 emission levels, and economic development levels. In addition, a mediation effect is found between the NAAQMN program and local PM2.5 emissions through the efficiency of environmental governance. The Chinese government should continue to promote the implementation of the NAAQMN program by promoting the NAAQMN program to the county and rural areas as well as adding the sites of the NAAQMN program in the existing cities. Also, during the process of promoting the NAAQMN program, sufficient differentiation in policies should be developed for different cities.

Association Between PM2.5 Exposure Level and Primary Open-Angle Glaucoma in Taiwanese Adults: A Nested Case-Control Study.

Primary open-angle glaucoma (POAG) is the most common type of glaucoma. However, little is known about POAG in adults and exposure to air pollution. The current study aims to investigate whether exposure to particulate matter with a mass median aerodynamic diameter of ≤2.5 μm (PM2.5) is associated with POAG diagnosis. Patient data were obtained from the Longitudinal Health Insurance Database 2010 (LHID2010) of Taiwan for the 2008–2013 period. PM2.5 concentration data, collected from the Ambient Air Quality Monitoring Network established by the Environmental Protection Administration of Taiwan, were categorized into four groups according to World Health Organization (WHO) exposure standards for PM2.5. We estimated the odds ratios (ORs) and 95% CIs for risk factors for POAG with logistic regression. The OR of per WHO standard level increase was 1.193 (95% CI 1.050–1.356). Compared with the normal level, the OR of WHO 2.0 level was 1.668 (95% CI 1.045–2.663, P < 0.05). After excluding confounding risk factors for POAG in this study, we determined that increased PM2.5 exposure is related to POAG risk (ORs > 1, P < 0.05). In this study, PM2.5 was an independent factor associated with open-angle glaucoma. Further research is required to better understand the mechanisms connecting PM2.5 and open-angle glaucoma.

Hygienic Assessment of Ambient Air Quality in Chita, a Priority Area of the Federal Clean Air Project

Introduction: The article is devoted to poor ambient air quality in some cities of the Russian Federation, one the most important social and environmental issues of today. The city of Chita is a priority area in term of ambient air pollution included in the Federal Clean Air Project of the National Ecology Project. Our objective was to assess air quality in residential areas of the city for a further improvement of the ambient air quality monitoring system and air quality action planning. Materials and methods: We analyzed the results of measuring ambient air quality at the monitoring sites of the government environmental monitoring system for 2014–2018 and statistical data on the volume and composition of industrial emissions. The spatial analysis of the impact of enterprises on residential areas was carried out in accordance with the guidelines of the Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing (Rospotrebnadzor). Results and discussion: We established high concentrations of benzo(a)pyrene (up to a 10.5-fold excess of the average daily maximum permissible concentration (MPC)), particulate matter (up to a 1.63-fold excess of the average daily MPC and a 11.4-fold excess of the single MPC), nitrogen dioxide (up to a 1.18-fold excess of the average daily MPC), hydrogen sulfide (up to a 11.94-fold excess of the single MPC), phenol, soot, and some other hazardous air contaminants. We determined priority sources of anthropogenic emissions and other factors contributing to poor air quality in Chita. Conclusions: Our findings indicate the necessity to enhance the existing ambient air quality monitoring network and programs, to assess spatial distribution of resulting exposure levels and population health risks, and to decide on the adequacy of the air quality action planning in the city of Chita in accordance with the comprehensive plan.

Dust and Radon Levels on the West Coast of Namibia – what did we learn?

Abstract The study investigated the potential for adverse health impacts from exposures to inhalable atmospheric dust and radon in the main towns along Namibia's central-western coast. An ambient air quality monitoring network was established at the end of 2016 to measure and track the inhalable dust (specifically PM10) and radon concentrations. Data collected between November 2016 and the end of December 2018 were assessed and selected PM10 samples were analysed for mineral and radionuclide content. In addition, emissions from anthropogenic sources were quantified and simulated using a regional dispersion model. Episodic dust storms associated with easterly winds are a common phenomenon during the winter months in the western part of Namibia. During such events, dust is transported over long distances westwards towards and well into the Atlantic Ocean. In view of the natural and anthropogenic nature of atmospheric dust, the study differentiated between sources of natural dust and those arising as a result of man-made processes. It was found that PM10 concentrations were, on average, higher at the coastal monitoring stations than at the stations located further inland, often exceeding the daily World Health Organisation (WHO) guideline value. Whilst high atmospheric dust concentrations were mostly associated with easterly wind conditions, sea salt was found to be a significant PM10 contributor at the coastal stations. Modelled results, which only included emissions from man-made sources, indicated that these sources contribute very little to the total PM10 concentrations measured at the coastal towns. The radiation exposure doses associated with the inhalation of atmospheric dust and radon were found to be well-below the world-wide average inhalation doses provided by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR).

Dust and radon levels on the west coast of Namibia – What did we learn?

The study investigated the potential for adverse health impacts from exposures to inhalable atmospheric dust and radon in the main towns along Namibia's central-western coast. An ambient air quality monitoring network was established at the end of 2016 to measure and track the inhalable dust (specifically PM10) and radon concentrations. Data collected between November 2016 and the end of December 2018 were assessed and selected PM10 samples were analysed for mineral and radionuclide content. In addition, emissions from anthropogenic sources were quantified and simulated using a regional dispersion model.Episodic dust storms associated with easterly winds are a common phenomenon during the winter months in the western part of Namibia. During such events, dust is transported over long distances westwards towards and well into the Atlantic Ocean. In view of the natural and anthropogenic nature of atmospheric dust, the study differentiated between sources of natural dust and those arising as a result of man-made processes.It was found that PM10 concentrations were, on average, higher at the coastal monitoring stations than at the stations located further inland, often exceeding the daily World Health Organisation (WHO) guideline value. Whilst high atmospheric dust concentrations were mostly associated with easterly wind conditions, sea salt was found to be a significant PM10 contributor at the coastal stations. Modelled results, which only included emissions from man-made sources, indicated that these sources contribute very little to the total PM10 concentrations measured at the coastal towns.The radiation exposure doses associated with the inhalation of atmospheric dust and radon were found to be well-below the world-wide average inhalation doses provided by the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR).

Low-Cost Air Quality Sensors: One-Year Field Comparative Measurement of Different Gas Sensors and Particle Counters with Reference Monitors at Tušimice Observatory

With attention increasing regarding the level of air pollution in different metropolitan and industrial areas worldwide, interest in expanding the monitoring networks by low-cost air quality sensors is also increasing. Although the role of these small and affordable sensors is rather supplementary, determination of the measurement uncertainty is one of the main questions of their applicability because there is no certificate for quality assurance of these non-reference technologies. This paper presents the results of almost one-year field testing measurements, when the data from different low-cost sensors (for SO2, NO2, O3, and CO: Cairclip, Envea, FR; for PM1, PM2.5, and PM10: PMS7003, Plantower, CHN, and OPC-N2, Alphasense, UK) were compared with co-located reference monitors used within the Czech national ambient air quality monitoring network. The results showed that in addition to the given reduced measurement accuracy of the sensors, the data quality depends on the early detection of defective units and changes caused by the effect of meteorological conditions (effect of air temperature and humidity on gas sensors and effect of air humidity with condensation conditions on particle counters), or by the interference of different pollutants (especially in gas sensors). Comparative measurement is necessary prior to each sensor’s field applications.

Influence of PM2.5 Exposure Level on the Association between Alzheimer's Disease and Allergic Rhinitis: A National Population-Based Cohort Study.

Alzheimer’s disease (AD) is an irreversible neurodegenerative disease that leads to dementia, health impairment, and high economic cost. Allergic rhinitis (AR) is a chronic inflammatory and allergic disease of the respiratory system that leads to health problems and has major effects on the daily lives of patients and their caregivers. Particulate matter (PM) refers to air pollutants 2.5 μm or less in diameter that are a source of concern because of their role in numerous diseases, including AR and other neurodegenerative diseases. To date, no study has demonstrated how PM2.5 exacerbates AR and results in AD. We conducted a national population-based cohort study by obtaining patient data from the National Health Insurance Research Database of Taiwan for the 2008–2013 period. PM2.5 concentration data were obtained from the ambient air quality monitoring network established by the Environmental Protection Administration of Taiwan. Monthly PM2.5 exposure levels were categorized into quartiles from Q1–Q4. The Cox proportional hazards analysis, after adjusting for age, sex, low income, and urbanization level, revealed that patients with AR had an elevated risk of developing AD (hazard ratio (HR): 2.008). In addition, the cumulative incidence of AD in the AR group was significantly higher than in the comparison group. The PM2.5 levels at Q2–Q4 (crude HR: 1.663–8.315; adjusted HR: 1.812–8.981) were stratified on the basis of the PM2.5 exposure group and revealed that AR patients exposed to PM2.5 are significantly prone to develop AD. In addition, the logistic regression analyses, after adjustment, demonstrated that an increase in the PM2.5 exposure level at Q2–Q4 (adjusted odds ratio (OR): 2.656–5.604) increased the risk of AR in AD patients. In conclusion, an increased PM2.5 exposure level could be correlated with AR, which could in turn cause AD. AR increased the risk of AD, in which exposure to PM2.5 increases the higher probability of AD.

Analysis of fine particle pollution data measured at 29 US diplomatic posts worldwide

Fine particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) is the most commonly measured ambient air pollutant worldwide on account of its adverse health effects. In 2008 the US Embassy in Beijing started monitoring PM2.5 on the embassy premises to provide US citizens in the area with actionable health information related to ambient air pollution. By May 2019 the United States Department of State ambient air quality monitoring network had expanded to 43 cities in 27 countries. This provided a reliable source of near-real-time PM2.5 data to fill data gaps in previously under-served countries.This manuscript describes data analyses of seasonal, diurnal and meteorological trends which can be used for air quality planning and awareness in 29 of the embassies host cities. People living, working, traveling or considering relocating to those areas can gain a better understanding of the air quality they are exposed to. Potential explanations are offered for sites that deviate from expected trends: Addis Ababa experiences high PM2.5 during the rainy season probably due to widespread biomass burning and light winds; Manama has high PM2.5 throughout the day on summer weekends because widespread air conditioner usage causes coal-fired power plants to operate at high capacity to meet the electricity demand; Pristina experienced very high PM2.5 during winter weekend nights, which turned out to be very cold and stagnant periods and likely increased wood and coal combustion for home heating.Although Chinese sites consistently exceed the country's annual PM2.5 standards, analysis of long term trends confirm steady improvements in air quality. These improvements cannot be attributed to favorable meteorological conditions alone and are likely due to reductions of emissions. Since random forest-based machine learning methods explained >90% of the variability in PM2.5 concentrations at 21 locations, they can be used in conjunction with meteorological models for air quality forecasting.

One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system

Abstract. China has been experiencing severe air pollution in recent decades. Although an ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research and Forecasting (WRF) model and the Community Multi-scale Air Quality (CMAQ) model was conducted to provide detailed temporal and spatial information of ozone (O3), total PM2.5, and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, overprediction of O3 generally occurs at low concentration range while underprediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in southern China than in northern China, central China, and Sichuan Basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of the CMAQ model to reproduce severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

Assessment of ambient air pollution in the Waterberg Priority Area 2012-2015

The Waterberg Priority Area ambient air quality monitoring network was established in 2012 to monitor the ambient air quality in the Waterberg Air Quality Priority Area. Three monitoring stations were established in Lephalale, Thabazimbi and Mokopane. The monitoring stations measure the concentrations of PM10, PM2.5, SO2, NOx, CO, O3, BTEX and meteorological parameters. Hourly data for a 31 month period (October 2012-April 2015) was obtained from the South African Air Quality Information System (SAAQIS) and analysed to assess patterns in atmospheric concentrations, including seasonal and diurnal patterns of the ambient concentrations and to assess the impacts that such reported pollution concentration may have. Local source regions for SO2, PM10, PM2.5 and O3 were identified and trends in the recorded concentrations are discussed.

Proposals for new data quality objectives to underpin ambient air quality monitoring networks

The data completeness requirements for the production of defensible annual averages of air quality parameters are discussed. Data quality objectives concerning time coverage and data capture specified by current legislation are critically examined, and the improved, combined metric of data coverage is proposed. Best practice proposals to ensure the representativeness of air quality data across the calendar year are presented. These are based on minimising any bias between the annual average calculated using the data acquired and the annual average that would have been calculated if data coverage had been 100 %. In all cases, the optimum solution is for air quality network operators to aim for 100 % data coverage.

Air quality at bus stop microenvironments in a Metro Vancouver urban and suburban area

&#x0D; BACKGROUND Those commuters waiting in small-scale transportation microenvironments, such as bus stops, can be exposed to levels of pollution higher than what is registered by ambient air quality monitoring stations. In addition, historically, those commuting in urban areas experience greater exposure to air pollutants than those commuting in suburban or rural areas, due to the nature of the environment. Little quantitative research has been conducted in the Metro Vancouver area regarding air quality in small scale transportation microenvironments. OBJECTIVES The aim of this study was to assess the differences in commuter exposure during AM Peak and PM Peak periods between an urban (Vancouver) and suburban (Ladner) bus stop. Furthermore, results were to be compared to the Metro Vancouver 24 hour rolling average objective as well as nearby Lower Fraser Valley (LFV) Ambient Air Quality Monitoring Network stations. METHODS The author measured particulate matter (PM) 2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter), using the DustTrakTM Aerosol Monitor 8520 between January 6, 2014 and January 21, 2014 on 12 weekdays, from 6:30am to 7:00am and 5:00pm to 5:30pm, at Stop #55165 Northbound Harvest Dr at Ladner Trunk Rd in Ladner, BC and from Stop #50043 Burrard Stn Bay1 in Vancouver, BC. In addition, meteorological conditions, traffic density, bus volume, and other observations were taken during sampling periods. RESULTS The author found that average PM2.5 exposures were highest during the morning in Ladner (μ=34.38667μg/m3) and lowest during the morning in Vancouver (μ=13.44 μg/m3). In addition, there was a statistically significant difference (p&lt;0.05) between Vancouver AM and the other groups (Ladner AM, Ladner PM [μ=28.07778 μg/m3], and Vancouver PM [μ=30.16667 μg/m3]), but the other groups were not significantly different from each other. Furthermore, the author found that the Vancouver AM average (μ=13.44 μg/m3) was below the Metro Vancouver 24 hour rolling average (25μg/m3) while all other groups (Ladner AM, Ladner PM, and Vancouver PM) exceeded this average. Lastly, when comparing all groups to the AM and PM hourly averages of their respective LFV Air Quality Monitoring Network stations (Ladner AM and PM vs. Tsawwassen AM and PM and Vancouver AM and PM vs. Kitsalano AM and PM), the author found that all groups averages exceeded the hourly averages of their respective stations. CONCLUSION Commuters’ peak hour exposures were significantly influenced by different microenvironments and were found to be higher than the ambient PM2.5 levels registered by the respective LFV Air Quality Monitoring Network stations. In order to address this, Metro Vancouver should implement personal exposure assessments, especially near roadways, to obtain actual levels of exposure to pollutants, such as PM2.5, by their residents. In this way, acute and chronic health outcome risks to air pollution can be better understood.&#x0D;

A census of the US near-roadway population: Public health and environmental justice considerations

This study estimates the size and distribution of the population living near high volume roads in the US, investigates race and income disparities in these near roadway populations, and considers the coverage of the national ambient air quality monitoring network. Every US census block is classified by traffic density and proximity to roads falling within several traffic volume ranges using year 2008 traffic data and the 2010 and 2000 US Census. The results indicate that 19% of the population lives near high volume roads. Nationally, greater traffic volume and density are associated with larger shares of non-white residents and lower median household incomes. Analysis at the county level finds wide variation in the size of near roadway populations and the severity of environmental justice concerns. Every state, however, has some population living near a high volume road and 84% of counties show some level of disparity. The results also suggest that most counties with residents living near high volume roads do not have a co-located regulatory air quality monitor.

Short-term effects of PM2.5, PM10 and PM2.5–10 on daily mortality in the Netherlands

IntroductionInformation on the relationship between levels of particulate matter (PM) smaller than 2.5μm and mortality rates in Europe is relatively sparse because of limited availability of PM2.5 measurement data. Even less information is available on the health effects attributable to PM2.5–10, especially for North-West Europe. ObjectivesTo investigate the relationship between various PM size fractions and daily mortality rates. MethodsDaily concentrations of PM from the Dutch National Ambient Air Quality Monitoring Network as well as all cause and cause-specific mortality rates in the Netherlands were obtained for the period 2008–2009. Poisson regression analysis using generalized additive models was used, with adjustment for potential confounding including long-term and seasonal trends, influenza incidence, meteorological variables, day of the week, and holidays.Different measures of PM (PM2.5, PM10 and PM2.5–10) were analysed. ResultsPM10 and PM2.5 levels were statistically significantly (p<0.05) associated with all cause and cause-specific deaths. For example, a 10μg/m3 increase in previous day PM was associated with 0.8% (95% CI 0.3–1.2) excess risk in all cause mortality for PM2.5 and a 0.6% (CI 0.2–1.0) excess risk for PM10. No appreciable associations were observed for PM2.5–10. Effects of PM10, and PM2.5 were insensitive to adjustment for PM2.5–10, and vice-versa. PM10 and PM2.5 were too highly correlated to disentangle their independent effects. ConclusionsPM10 and PM2.5 both were significantly associated with all cause and cause-specific mortality. We were unable to demonstrate significant effects for PM2.5–10, possibly due to the lower temporal variability and the higher exposure misclassification in PM2.5–10 compared to PM10 or PM2.5. The lack of effects of PM2.5–10 in our study should therefore not be interpreted as an indication that PM2.5–10 can be considered harmless.