Air Quality Monitoring Station Data Research Articles

COVID-19 Lockdown: Lessons learnt using multiple air quality monitoring station data from Kolkata city in India

On March 16, 2020, Kolkata megacity in India announced partial lockdown due to COVID-19 crisis. This study presents an analysis for multiple pollutants with special focus on NO2 and O3 based on data from different monitoring stations located to across Kolkata city for the period from 16 March- 17 May 2020 compared to the pre-lockdown period. Most significant reduction was observed in the concentration of nitrogen dioxide (NO2) (- 76.8%), volatile organic compounds (VOCs) (- 69.5%), PM10 (- 64.6%) and PM2.5 (- 60.9%). A lower reduction percentage was found for CO, sulfur dioxide (SO2) and ammonia (-48.6%, - 41.7% and – 41.1% respectively). However, during partial lockdown, lockdown phase-1, phase-2 and phase-3, surface-level ozone (O3) has changed respectively by 31.72%, 31.13%, -14.28% and -14.05% which resulted in an overall increase of 8.17% in the entire study period. The air quality index (AQI) in Kolkata which usually remains poor or very poor, even during lockdown period, failed to attain the ‘good’ standard. This needs special attention in human health impact assessment and public health management. We recommend additional attention to be drawn towards stickiness in O3 which had adverse human health and which went up during lockdown period compared to pre-lockdown period. We highlight some major policy implications of the observed trends to combat city air pollution along with climate co-benefits by shifting transport fuel and related infrastructure.

Assessment and Improvement of Two Low-Cost Particulate Matter Sensor Systems by Using Spatial Interpolation Data from Air Quality Monitoring Stations

Two low-cost fine particulate matter (PM2.5) sensor systems have been established by the government and community in Taiwan. Each system combines hundreds of PM2.5 sensors through an Internet of Things architecture. Since these sensors have not been calibrated, their performance has been questioned. In this study, the spatial interpolation data from air quality monitoring stations (AQMSs) was used to quantify the performances of the two sensor systems. The linearity, sensitivity, offset, precision, accuracy, and bias of the two sensor systems were estimated. The results indicate that the linearity of the government’s sensor system was higher than that of the community sensor system. However, the sensitivity of the government’s system was lower than that of the community system. The relative standard deviation, relative error, offset, and bias of the community sensor system were higher than those of the government sensor system. However, the government sensor system exhibited superior spatial interpolation results for the AQMS data than the community sensor system did. The precision and accuracy of the two sensor systems were poor during a period of low PM2.5 concentrations. A working platform of improvements consisting of monitoring the operation loop and automatic correction loop is proposed. The monitoring operation loop comprises five modules, namely outlier detection, temporal anomaly analysis, spatial anomaly analysis, spatiotemporal anomaly analysis, and trajectory analysis modules. The automatic correction loop contains spatial interpolation module, a sensor performance detection module, and a correction module. The proposed working platform can enhance the performance of low-cost sensor systems, especially as alert systems for reportable events.

Fusion of land use regression modeling output and wireless distributed sensor network measurements into a high spatiotemporally-resolved NO2 product.

Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO2) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15min sampling frequency) made by an array of low-cost sensor nodes, with the sensors' readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3ppb, smaller than the RMSE of the two base products independently (LUR: 14.87ppb, WDSN: 10.45ppb). The normalized Moran's I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO2 concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.

Ambient air particulate matter (PM10) satellite monitoring and respiratory health effects assessment.

Air particulate matter with an aerodynamic diameter of 10µm or less (PM10) is one of the main causes of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This study explored the relationship between PM10 by remote sensing and AECOPD in Chaharmahal-o-Bakhtiari province from 2014 to2018. PM10 concentrations were predicted and validated based on aerosol optical depth (AOD) from 161 images processed by MODIS sensor and ground air quality monitoring station data. Demographic information and spirometric indices of 2038 patients with AECOPD were collected and analyzed from the hospital during the studied periods. SPSS software was used to analyze the relationships between these two categories of information. There was a significant negative relationship between PM10 and FVC, FVC%, FEV1, FEV1%, FEF25-75, FEV1/FVC, PEF, and FEF25FVC indices (p < 0.05). The results showed that over 2014-2018, the annual mean of PM10 concentrations varied from 35 to 52µg/m3. The result of the regression model showed that the patient's age, body mass index (BMI), and PM10 concentrations were the most affecting variables on the two important spirometric indices i.e., FVC% and FEV1%. The PM10 concentrations and number of AECOPD patients had a similar pattern during the studied period. The women group, age group above 74 years, normal BMI, and non-smoking patients showed the most sensitivity to the PM10 concentrations. Our findings provide supplementary scientific information on PM10 concentration related to the incidence of AECOPD and as a variable affecting the most important spirometry indicators by providing local decision-makers information needed to set a priority of air pollution control measures as well as health services.

충남지역 PM10과 PM2.5 농도의 시공간 분포 특징

Many large-scale air pollution sources such as a steel mill, coal-fired power stations, and petrochemical plant are located in ChungNam (CN) area. In this study, spatial and temporal distributions of PM10 and PM2.5 concentrations were analyzed using 1-h average data obtained from 29 air quality monitoring stations (AQMS) located in CN area, where is divided by three regions such as northern, southern, and southeastern regions. Among them, the highest PM10 and PM2.5 concentrations were observed in northern region where is influenced by motor vehicles in highly populated Cheonan and Asan, and large emission sources in Dangjin. Recently, annual average PM10 concentrations of CN are lower than national standard, but they still do not meet local standard. And, annual average PM2.5 concentrations of CN are over both national and local standards. A significant difference was found among AQMSs in the same province because each AQMS might be heavily influenced by local emission sources. So, data from AQMSs located near the high emission source should be carefully interpreted. Considering distribution of various emission sources, a large number of AQMS data can be useful in analyzing local air pollution in detail.

Hot - Spot summertime levels and potential sources of volatile organic compounds (VOC) on roads around Çanakkale and Kilitbahir Harbors across Dardanelles Strait

Organic air pollutants have considerable influence particularly on atmospheric chemistry, compared to other air pollutants. Traffic is one of the major sources of air pollutants occurred in Çanakkale atmosphere by both remarkable road and marine traffic loads around the harbors. The aims of this study were finding the daily, week/weekend, and spatial variations of VOC at multiple sampling points of Çanakkale and Kilitbahir. In this study, active air samples were collected from the major roads of Kilitbahir and Çanakkale Harbors during rush hours and off-peak in 2018 summer. According to the measurement results, average levels of total volatile organic compounds (TVOC) were around 129 μg/m3 in Çanakkale and 93 μg/m3 in Kilitbahir. Toluene was the most abundant compound. Average B: T: E: X ratio was calculated as 2.4:7.0:1:5.5 in Çanakkale and 3.2:7.6:1:5.2 in Kilitbahir. According to the sampling points of both locations, levels of TVOC, benzene, toluene, and m,p-xylene in Çanakkale and toluene levels in Kilitbahir showed statistically significant differences (p < 0.05). Also, benzene, which was already classified as a human carcinogen, varied diurnally in Kilitbahir (p < 0.05). Toluene levels varied between week and weekend days (p < 0.05). Moreover, measured VOC levels were compared with air quality monitoring station (AQMS) data and meteorological parameters. TVOC levels showed positive correlations with SO2 and PM10 (p < 0.05). Furthermore, a multivariate factor analysis applied to data set including VOC measurements, AQMS data, and meteorological parameters implied that traffic is the predominant factor influencing the air quality around the study sites.

Analysis of a Pollution Transmission Process in Hefei City Based on Mobile Lidar

The air pollution has been regional in China with the development of economy. To monitoring the air pollution transmission, a new technique, mobile lidar system (GBQ-S01), was introduced. In this paper, a pollution transmission process happened on October 26th, 2017, was analyzed with the use of mobile lidar, air quality monitoring stations data, and Hysplit backward trajectories. The results showed that the polluted air mass was transferred from northeast under the force of air pressure. Under the influences of air pollution transmission and bad meteorological diffusion conditions, The PM10 quality concentrations in Hefei increased a lot within 5 hours; among all the 10 national air quality monitoring stations, the Luyang District (the northernmost one) and Changjiang Middle Road (the easternmost one) received the most serious impact with PM10 concentration reached up to 252 μg/m3 and 219 μg/m3 at 22:00 (Beijing Time).

Spatial estimation of surface ozone concentrations in Quito Ecuador with remote sensing data, air pollution measurements and meteorological variables.

Surface ozone is problematic to air pollution. It influences respiratory health. The air quality monitoring stations measure pollutants as surface ozone, but they are sometimes insufficient or do not have an adequate distribution for understanding the spatial distribution of pollutants in an urban area. In recent years, some projects have found a connection between remote sensing, air quality and health data. In this study, we apply an empirical land use regression (LUR) model to retrieve surface ozone in Quito. The model considers remote sensing data, air pollution measurements and meteorological variables. The objective is to use all available Landsat 8 images from 2014 and the air quality monitoring station data during the same dates of image acquisition. Nineteen input variables were considered, selecting by a stepwise regression and modelling with a partial least square (PLS) regression to avoid multicollinearity. The final surface ozone model includes ten independent variables and presents a coefficient of determination (R2) of 0.768. The model proposed help to understand the spatial concentration of surface ozone in Quito with a better spatial resolution.

Detection of aerosol pollution sources during sandstorms in Northwestern China using remote sensed and model simulated data

The present paper has used a comprehensive approach to study atmosphere pollution sources including the study of vertical distribution characteristics, the epicenters of occurrence and transport of atmospheric aerosol in North-West China under intensive dust storm registered in all cities of the region in April 2014. To achieve this goal, the remote sensing data using Moderate Resolution Imaging Spectroradiometer satellite (MODIS) as well as model-simulated data, were used, which facilitate tracking the sources, routes, and spatial extent of dust storms. The results of the study have shown strong territory pollution with aerosol during sandstorm. According to ground-based air quality monitoring stations data, concentrations of PM10 and PM2.5 exceeded 400 μg/m3 and 150 μg/m3, respectively, the ratio PM2.5/PM10 being within the range of 0.123–0.661. According to MODIS/Terra Collection 6 Level-2 aerosol products data and the Deep Blue algorithm data, the aerosol optical depth (AOD) at 550 nm in the pollution epicenter was within 0.75–1. The vertical distribution of aerosols indicates that the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) 532 nm total attenuates backscatter coefficient ranges from 0.01 to 0.0001 km−1 × sr−1 with the distribution of the main types of aerosols in the troposphere of the region within 0–12.5 km, where the most severe aerosol contamination is observed in the lower troposphere (at 3–6 km). According to satellite sounding and model-simulated data, the sources of pollution are the deserted regions of Northern and Northwestern China.

Effect of cloud-to-ground lightning and meteorological conditions on surface NOx and O3 in Hong Kong

Cloud-to-ground (CG) lightning, meteorological conditions and corresponding surface nitrogen oxides (NOx) and ozone (O3) variations in relation to thunderstorm and lightning activities over Hong Kong at Kwai Chung (urban), Tung Chung (new town) and Tap Mun (background) during active lightning seasons from 2009 to 2013 were studied by analyzing respective air quality monitoring station data along with CG lightning and meteorological data. We observed NOx enhancement and significant O3 decline on lightning days. Influences of land use types, lightning activities and meteorological conditions on surface NOx and O3 were examined. NOx and O3 concentrations shifted towards higher and lower levels, respectively, during lightning days especially in the dominant wind directions. Principal component analysis/absolute principal component scores (PCA/APCS) method and stepwise multiple linear regression (MLR) analysis were employed to examine the influence of thunderstorm related lightning and meteorological parameters on surface NOx and O3. Wind speed was supposed to be the most important meteorological parameter affecting the concentration of NOx, and lightning activities were observed to make a positive contribution to NOx. Negative contribution of hot, cloudy and wet weather and positive contribution of wind speed were found to affect the concentration of O3. Lightning parameters were also found to make a small positive contribution to O3 concentration at Tap Mun and Tung Chung, but the net effect of lightning activities and corresponding meteorological conditions was the decrease of O3 on lightning days. Reasonably good agreement between the predicted and observed NOx and O3 values indicates that PCA/APCS-MLR is a valuable method to study the thunderstorm induced NOx and O3 variations.

Estimating multi-annual PM2.5 air pollution levels using sVOC soil tests: Ashkelon South, Israel as a case study

Background and aimsIn most developed countries, air pollution is monitored by stationary networks of air quality monitoring stations (AQMS). Due to high installation and maintenance costs, the number of such stations (especially those measuring particulate matter (PM) is often insufficient, placing limitations on the geographic coverage of air pollution monitoring and the accuracy of exposure assessment studies based on AQMS data. Semi-volatile organic compounds (sVOCs) often spread with PM and contribute to the PM composition when emitted into the atmosphere. As a result, sVOC concentrations in soil can help, as we hypothesize, to estimate average levels of PM air pollution in geographic areas in which PM monitoring by AQMS is sparse or unavailable. MethodsSoil samples taken around the Ashkelon-South industrial area in Israel were tested for the presence of sVOCs compounds using a standard EPA 8270C protocol. PM pollution data obtained from local AQMSs were then mutually compared with sVOC soil contamination levels observed in the same locations, using spatial interpolation methods and multivariate statistical analysis tools. ResultsPM levels in the atmosphere, estimated using sVOC soil concentrations, and adjusted for several locational attributes and average levels of other commonly monitored air pollutants, revealed a reasonably strong association with PM2.5 averages actually recorded by AQMS, helping to explain 86–88% of the observed PM variation (R2-Adj. = 0.859–0.879), depending on the model type. ConclusionsThe study confirms feasibility of obtaining reasonably accurate PM air pollution estimates using sVOC soil testing. While the costs of establishment and maintenance of a dense network of AQMS may be prohibitive, the proposed approach may help to obtain reasonably accurate PM air pollution estimates for geographic areas in which PM monitoring by AQMS is sparse or unavailable.

How to assess exposure to atmospheric pollutants in epidemiological studies? An illustration of some trade-offs in the context of pregnancy exposures

Background: The concerns regarding possible effects of maternal exposure to atmospheric pollutants on pregnancy outcomes warrant effort in exposure assessment. Aims: We review and discuss the advantages and limitations of the various tools considered in epidemiology to assess air pollution exposure during pregnancy, focusing on outdoor models and tools for personalised exposure assessment. Methods and results: Models based on air quality monitoring station (AQMS) networks have a poor spatial resolution. Land-use regression (LUR) models have an improved spatial resolution but classically provide a yearly exposure estimate. We have suggested to combine LUR models with AQMS data to increase their temporal resolution. As shown in the context of Eden mother-child cohort, these seasonally-adjusted LUR models are in good agreement with dispersion models to predict pregnancy average exposure to pollutants such as NO2. The main limitations of such approaches are that 1) space-time activity and 2) indoor exposures are not taken into account. In a newly set mother-child cohort, SEPAGES-feasibility, women carried GPS and personal NO2 dosimeters and indoor measures have been conducted. We provide estimates of the amount of exposure misclassification attributable to these two sources of error, compared to approaches based solely on outdoors model-based estimates at the home address. Discussion: The progress in the assessment of exposure of pregnant women to atmospheric pollutants in the last decades have allowed refining exposure estimates and integrating time-, space- and behaviour-related exposure contrasts, at the cost of increase in logistic and budget constraints. These approaches may be cumbersome to implement on a large scale (to study rare outcomes); for such outcomes, detailed assessment of exposure using personal tools in a sub-population, combined to measurement error models to translate results in the larger population may be a good compromise.

Associations between outdoor air quality and birth weight: a geostatistical sequential simulation approach in Coastal Alentejo, Portugal

Several environmental health studies suggest birth weight is associated with outdoor air pollution during gestation. In these studies, exposure assignments are usually based on measurements collected at air quality monitoring stations that do not coincide with health data locations. So, estimated exposures can be misleading if they do not take into account the uncertainty of exposure estimates. In this article we conducted a semi-ecological study to analyze associations between air quality during gestation and birth weight. Air quality during gestation was measured using a biomonitor, as an alternative to traditional air quality monitoring stations data, in order to increase spatial resolution of exposure measurements. To our knowledge this is the first time that the association between air quality and birth weight is studied using biomonitors. To address exposure uncertainty at health locations, we applied geostatistical simulation on biomonitoring data that provided multiple equally probable realizations of biomonitoring data, with reproduction of observed histogram and spatial covariance while matching for conditioning data. Each simulation represented a measure of exposure at each location. The set of simulations provided a measure of exposure uncertainty at each location. To incorporate uncertainty in our analysis we used generalized linear models, fitted simulation outputs and health data on birth weights and assessed statistical significance of exposure parameter using non-parametric bootstrap techniques. We found a positive association between air quality and birth weight. However, this association was not statistically significant. We also found a modest but significant association between air quality and birth weight among babies exposed to gestational tobacco smoke.

Recent Trends and Future Projections in Asian Air Pollution

We studied trends in Asian air pollution in recent decades using air-quality monitoring station data, satellite retrieval data (GOME NO2), and regional-scale chemical transport model (CTM) simulation. A newly developed annual Asian-scale emission inventory (REAS) from 1980-2003 was used in observation data analysis and CTM. Analyses of recent trends in annual emissions in China by REAS and satellite GOME NO2 show an 8-10% increase after 2000 suggesting the impact of long-range transport of secondary air pollutants in regions and countries downwind. Detailed analyses of O3 observation data in Japan suggest an annual averaged O3 concentration increase of 2% yr-1 due to this long-range transport. We extended our regional air quality study targeting 2020. REAS provides three emission scenarios for China: the reference case (REF), the policy success case (PSC), and the policy failure case (PFC). Projected REF emissions for 2020 show O3 concentrations rising to 75 to 90 ppbv in June and 75 to 85 ppbv in August over the North China Plain. Projected PFC emissions bring an increase of monthly averaged O3 with greater than 20 ppbv (1 ppbv yr-1 growth) in the North China Plain. Surface O3 under the PFC scenario is enhanced by 6 to 8 ppbv over the Korean Peninsula and by 2 to 6 ppbv in Japan from 2000 to 2020 despite the reduction of NOx in Japan. This may become a critical level in air quality in Asia.

Outdoor air pollution and children's respiratory symptoms in the steel cities of New South Wales.

To investigate the relationship between outdoor air pollution and the respiratory health of children aged 8 to 10 years. A cross-sectional survey (between October 1993 and December 1993) of children's health and home environment. Summary measures of particulate pollution (levels of particles with an aerodynamic diameter less than 10 microns [PM10] each 6th day) and SO2 (daily mean and maximum hourly values) were estimated for each area (using air quality monitoring station data from July 1993 to June 1994). SETTING AND SURVEY PARTICIPANTS: Parents of 3023 primary school children (Years 3, 4 and 5) from industrial and non-industrial areas with air quality monitoring stations in the Hunter and Illawarra regions of New South Wales. Reported occurrence of four or more chest colds, four or more attacks of wheezing, and night-time cough without a cold for more than two weeks, all within the previous 12 months. 77% response rate, ranging by area from 66% to 88%. The average annual outdoor air pollution for the nine areas was 18.6-43.7 micrograms/m3 for PM10 and 0.16-0.90 parts per hundred million for SO2. The proportion of children reported to have the main outcome symptoms were: chest colds, 3.0%-9.7%; night cough, 12.3%-30.5%; and wheeze, 3.4%-11.3%. There was no significant association with SO2, but a significant increase in the odds of symptoms per 10 micrograms/m3 increase in PM10 on chest colds (odds ratio [OR], 1.43; 95% confidence interval [CI], 1.12-1.82) and night-time cough (OR, 1.34; 95% CI, 1.19-1.53), but not wheeze. Passive smoking was significantly associated with chest colds but not with the other symptoms. Maternal allergy was associated with all three respiratory symptoms, most strongly with wheeze. These results provide evidence of health effects at lower than expected levels of outdoor air pollution in the Australian setting. They also suggest differences in contributions of environmental and hereditary factors to cough and chest colds compared with wheeze.

Analysis of wintertime NO 2 pollution in the Tokyo metropolitan area

The high concentration of NO 2 in the Tokyo Metropolitan area was investigated by means of air quality monitoring station data analysis and two numerical models: a photochemical box model and a photochemical grid model. Based on monitoring data, the typical variation with time of NO, NO 2 and O 3 for days with high NO 2 concentrations is presented. The application of the photochemical box and photochemical grid models successfully reproduced these variations when using typical emission intensities for NO x and NMHC from the Tokyo downtown area. Model results revealed that the high concentrations of NO 2 in winter (exceeding the Japan environmental quality standard) mainly result from photochemical oxidation of NO to NO 2.