Urban Air Quality Monitoring Network Research Articles

Characterization of Spatial and Temporal Variations in Air Pollutants and Identification of Health Risks in Xi’an, a Heavily Polluted City in China

The study of the temporal and spatial characteristics of air pollutants in heavily polluted cities is extremely important for analyzing the causes of pollution and achieving a viable means of control. Such characteristics in the case of Xi’an, a typical heavily polluted city in Fenwei Plain, China, have remained unclear due to limitations in data accuracy and research methods. The monthly, daily, and hourly patterns of O3 and particulate matter (PM2.5 and PM10) are analyzed in this study using on-site data provided by an urban air quality monitoring network. The analysis of variance (ANOVA) method was used to compare differences in pollutant concentrations during different seasons and time periods. The spatial distributions of O3, PM2.5, and PM10 at different time points following interpolation of the air quality monitoring sites have been analyzed. The results show that the O3 concentration from 12 p.m. to 3 p.m. was significantly higher than that in the morning and evening, and the concentrations of PM2.5 and PM10 from 7 p.m. to 10 p.m. were significantly higher than those in the morning and afternoon. The number of qualified days for PM2.5 was less than 30 and unqualified days for O3 was more than 100 in 2019. There is a potential risk of exposure to pollution with associated health risks. Even on the same day, the spatial pollutant distributions at different time points can differ significantly. This study provides a scientific basis for reducing O3 and particulate matter exposure. Outdoor activities in the morning in summer are more beneficial to reduce O3 exposure, and outdoor activities should be curtailed in the evening in winter to reduce particulate exposure. This study provides a scientific basis for the government to formulate public health policies to reduce pollution exposure from outdoor activities.

An optimized development of urban air quality monitoring network design based on particulate matters.

The design of an air quality monitoring network (AQMN) is the mandatory step to manage air pollution in megacities. Several studies are being done on the location selection of AQMNs based on topography, meteorology, and pollution density. Still, the critical research gap that needs to be addressed is the role of pollutants' importance and prioritization in AQMN. This study aims to utilize the sphere of influence (SOI) method to design an AQMN in a megacity based on particulate matter (PM) as the most serious urban pollutant. Model evaluation was done by employing annual emission inventory data of PM in Tabriz, an industrial and crowded megacity with high exposure to salt particulates, considering 3549 square blocks with a size of 500m * 500m. Then, the SOI methodology utilizing the utility function (UF) approach is applied using MATLAB software calculations to determine optimal air quality monitoring network configurations. A range of numbers of utility functions was yielded for every spot on the map. It resulted in grid city maps with final spots for PM10, PM2.5, and intersecting spots. As a result, ten sites are selected as the best possible locations for the AQMN of a 2 million population city. These results could play a precise and significant role in urban air quality decision-making and management.

Decomposing the Temporal Signature of Nitrogen Dioxide Declines during the COVID-19 Pandemic in UK Urban Areas

On March 23, 2020, a national lockdown was imposed in the UK to limit interpersonal contact and the spread of COVID-19. Human mobility patterns were drastically adjusted as individuals complied with stay-at-home orders, changed their working patterns, and moved increasingly in the proximity of their home. Such behavioural changes brought about many spillover impacts, among which the sharp and immediate reduction in the concentration of nitrogen-based pollutants throughout the country. This work explores the extent to which urban Nitrogen Dioxide (NO2) concentration responds to changes in human behaviour, in particular human mobility patterns and commuting. We model the dynamic and responsive change in NO2 concentration in the period directly following national lockdown and respective opening orders. Using the national urban air quality monitoring network we generate a synthetic NO2 concentration series built from a time series of historic data to compare expected modelled trends to the actual observed patterns in 2020. A series of pre- and post-estimators are modelled to understand the scale of concentration responsiveness to human activity and varying ability of areas across the UK to comply with the lockdown closing and response to openings. Specifically, these are linked to workday commuting times and observed patterns of human mobility change obtained from Google mobility reports. We find a strong and robust co-movement of air pollution concentration and work-related mobility – concentrations of NO2 during typical weekday commuting hours saw a higher relative drop, moving in tandem with patterns of human mobility around workplaces over the course of lockdowns and openings. While NO2 concentrations remained relatively low around the time of reopening, particularly during commuting hours, there is a relatively fast responsiveness rate to concentrations increasing quickly in line with human activity. With one of the key Government advice for workers to take staggered transportation into work and lessen the burden of rush hours and adopting more flexible work-home arrangements, our results would suggest that reductions in NO2 in urban areas are particularly responsive to broader human patterns and dynamics over time as we transitioned towards new working routines.

Time-Dependent Downscaling of PM2.5 Predictions from CAMS Air Quality Models to Urban Monitoring Sites in Budapest

Budapest, the capital of Hungary, has been facing serious air pollution episodes in the heating season similar to other metropolises. In the city a dense urban air quality monitoring network is available; however, air quality prediction is still challenging. For this purpose, 24-h PM2.5 forecasts obtained from seven individual models of the Copernicus Atmosphere Monitoring Service (CAMS) were downscaled by using hourly measurements at six urban monitoring sites in Budapest for the heating season of 2018–2019. A 10-day long training period was applied to fit spatially consistent model weights in a linear combination of CAMS models for each day, and the 10-day additive bias was also corrected. Results were compared to the CAMS ensemble median, the 10-day bias-corrected CAMS ensemble median, and the 24-h persistence. Downscaling reduced the root mean square error (RMSE) by 1.4 µg/m3 for the heating season and by 4.3 µg/m3 for episodes compared to the CAMS ensemble, mainly by eliminating the general underestimation of PM2.5 peaks. As a side-effect, an overestimation was introduced in rapidly clearing conditions. Although the bias-corrected ensemble and model fusion had similar overall performance, the latter was more efficient in episodes. Downscaling of the CAMS models was found to be capable and necessary to capture high wintertime PM2.5 concentrations for the short-range air quality prediction in Budapest.

China's Complex Urban Air Pollution: An Improved Understanding with Ground Operational Measurements.

Complex urban air quality has long been assessed by a single (or major) contaminant, for example, fine particulate matter (PM2.5), but scant attention has been given to multicontaminant air pollution, especially in countries with severe air pollution, for example, China. We thus proposed an improved method for quantifying both single- and multicontaminant air pollution. Our approach uses China's major cities as an example because they have an operational national urban air quality monitoring network. We found that our proposed method could remove the duplicated consideration under both single- and multicontaminant conditions, thus proving to be an improved and more accurate way to understand complex urban air pollution conditions. Our method involved monitoring 3 contaminants (PM2.5, PM10, and SO2 ) in cities in Shanxi, Shandong, Henan, and Hebei Provinces and 2 contaminants (PM2.5 and PM10) in the cities between the Yellow River and the Yangtze River, and these pollutants were the major contributors to multicontaminant air pollution. We argue that both the research community and the government should pay increased attention to multicontaminant air pollution beyond the current single major pollutant-based air pollution method when building a sustainable city. Integr Environ Assess Manag 2020;16:306-313. © 2020 SETAC.

Applying linear and nonlinear models for the estimation of particulate matter variability

In this study, data collected from an urban air quality monitoring network are being used for the purpose of evaluating various methodologies used for spatial interpolation in the context of proposing an effective yet simple to apply scheme for PM spatial point estimations. The examined methods are the Inverse Distance Weighting, two linear regression models, the Multiple Linear Regression and the Linear Mixed Model, along with a Feed Forward Neural Network (FFNN) model. These schemes utilize daily PM10 and PM2.5 concentrations collected from five and three air quality monitoring sites respectively. In order to obtain the resulted estimations, the leave-one-out cross-validation methodology is used for all methods. The evaluation of their predictive ability is performed by using a combination of difference and correlation statistical measures, scatter plots and statistical tests. The results indicate the usefulness of FFNNs as they are found to be statistically significantly superior for modelling the particulate matter spatial variability. The model performance statistics show that in most cases the error values are considerably lower for the FFNN model. Additionally, the rank and Wilcoxon rank tests reveal that the null hypothesis for equal predictive accuracy is rejected for the majority of monitoring sites and schemes (values lower than the critical t-value). According to the comparison results, the FFNN model is selected for forecasting air quality limit exceedances set by the European Union and World Health Organization air quality standards. For two monitoring sites in which the largest number of exceedances occurred, the probability of detection is high while the probability of false detection is very low, further establishing the neural networks’ predictive ability.

Spatial estimation of urban air pollution with the use of artificial neural network models

The deterioration of urban air quality is considered worldwide one of the primary environmental issues and scientific evidence associates the exposure to ambient air pollution with serious health effects. This fact highlights the importance of generating accurate fields of air pollution for quantifying present and future health related risks. Interpolation methods for point estimations in the field of air pollution modelling enable the estimation of pollutant concentrations in unmonitored locations. The main objective of this study is to evaluate two interpolation methodologies, Artificial Neural Networks and Multiple Linear Regression, using data from a real urban air quality monitoring network located at the greater area of metropolitan Athens in Greece. The results for five regulated air pollutants (Nitrogen dioxide, Nitrogen monoxide, Ozone, Carbon monoxide and Sulphur dioxide) are compared through the use of a set of correlation and difference statistical measures and residuals distribution. Artificial neural networks are found in most cases to be significantly superior, especially where the air quality network density is limited, leading to a decreased degree of spatial correlations among the monitoring sites.

Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm

Air quality monitoring networks play a significant role in identifying the spatiotemporal patterns of air pollution, and they need to be deployed efficiently, with a minimum number of sites. The revision and optimal adjustment of existing monitoring networks is crucial for cities that have undergone rapid urban expansion and experience temporal variations in pollution patterns. The approach based on the Weather Research and Forecasting–California PUFF (WRF-CALPUFF) model and genetic algorithm (GA) was developed to design an optimal monitoring network. The maximization of coverage with minimum overlap and the ability to detect violations of standards were developed as the design objectives for redistributed networks. The non-dominated sorting genetic algorithm was applied to optimize the network size and site locations simultaneously for Shijiazhuang city, one of the most polluted cities in China. The assessment on the current network identified the insufficient spatial coverage of SO2 and NO2 monitoring for the expanding city. The optimization results showed that significant improvements were achieved in multiple objectives by redistributing the original network. Efficient coverage of the resulting designs improved to 60.99% and 76.06% of the urban area for SO2 and NO2, respectively. The redistributing design for multi-pollutant including 8 sites was also proposed, with the spatial representation covered 52.30% of the urban area and the overlapped areas decreased by 85.87% compared with the original network. The abilities to detect violations of standards were not improved as much as the other two objectives due to the conflicting nature between the multiple objectives. Additionally, the results demonstrated that the algorithm was slightly sensitive to the parameter settings, with the number of generations presented the most significant effect. Overall, our study presents an effective and feasible procedure for air quality network optimization at a city scale.

Air quality improvement in a megacity: implications from 2015 Beijing Parade Blue pollution control actions

Abstract. The implementation of strict emission control measures in Beijing and surrounding regions during the 2015 China Victory Day Parade provided a valuable opportunity to investigate related air quality improvements in a megacity. We measured NH3, NO2 and PM2.5 at multiple sites in and outside Beijing and summarized concentrations of PM2.5, PM10, NO2, SO2 and CO in 291 cities across China from a national urban air quality monitoring network between August and September 2015. Consistently significant reductions of 12–35 % for NH3 and 33–59 % for NO2 in different areas of Beijing during the emission control period (referred to as the Parade Blue period) were observed compared with measurements in the pre- and post-Parade Blue periods without emission controls. Average NH3 and NO2 concentrations at sites near traffic were strongly correlated and showed positive and significant responses to traffic reduction measures, suggesting that traffic is an important source of both NH3 and NOx in urban Beijing. Daily concentrations of PM2.5 and secondary inorganic aerosol (sulfate, ammonium and nitrate) at the urban and rural sites both decreased during the Parade Blue period. During (after) the emission control period, concentrations of PM2.5, PM10, NO2, SO2 and CO from the national city-monitoring network showed the largest decrease (increase) of 34–72 % (50–214 %) in Beijing, a smaller decrease (a moderate increase) of 1–32 % (16–44 %) in emission control regions outside Beijing and an increase (decrease) of 6–16 % (−2–7 %) in non-emission-control regions of China. Integrated analysis of modelling and monitoring results demonstrated that emission control measures made a major contribution to air quality improvement in Beijing compared with a minor contribution from favourable meteorological conditions during the Parade Blue period. These results show that controls of secondary aerosol precursors (NH3, SO2 and NOx) locally and regionally are key to curbing air pollution in Beijing and probably in other mega cities worldwide.

Particulate matter in the rural settlement during winter time

The objective of this study was to analyzed the variability of the ambient particulates mass concentration in an area occupied by rural development. The analysis applied daily and hourly PM 2.5 and PM 10 levels. Data were derived on the basis of measurement results with the application of stationary gravimetric samplers and optical dust meter. The obtained data were compared with the results from the urban air quality monitoring network in Opole. Principal Component Analysis was used for data analysis. Research hypotheses were checked using U Mann-Whitney. It was indicated that during the smog episodes, the ratio of the inhalable dust fraction in the rural aerosol is greater than for the case of the urban aerosol. It was established that the principal meteorological factors affecting the local air quality. Air temperature, atmospheric pressure, movement of air masses and occurrence of precipitation are the most important. It was demonstrated that the during the temperature inversion phenomenon, the values of the hourly and daily mass concentration of PM 2.5 and PM 10 are very improper. The decrease of the PM's concentration to a safe level is principally relative to the occurrence of wind and precipitation.

Essential Tasks to Reduce Environmental Pollution in Urban Transportation Networks

This article reviewed the impacts of vehicle emissions in urban transportation network, the existing strategies to reduce such transportation emissions, the complexity of urban transportation environment network, and the development of vehicle emission models. The vanguard research needs are also proposed in developing emission and air quality models and simulation, and emission-oriented transportation management strategies. This requires multidisciplinary research for the expectant counter-measures for urban transportation environmental network systems. Three essential tasks are proposed including: (1) improving urban air quality monitoring networks, (2) refining field-oriented emission models to better addressing real-time transportation activities, and (3) developing efficient and smart traffic control strategies to mitigate emission and improve air quality. These tasks would address the emerging issues associated with the sustainability and reliability of urban transportation environmental network, through interdisciplinary efforts represented by Science, Technology, Engineering, and Mathematics (STEM) disciplines, which would involve the knowledge of transportation, environment, mathematics, chemistry, physics, engineering technology, and computer science.

Evaluation of Air Pollution Monitoring Networks in Seoul Metropolitan Area using Multivariate Analysis

The adequacy of urban air quality monitoring networks in the largest metropolitan city, Seoul was evaluated using multivariate analysis for , , CO, PM10, and . Through cluster analysis for 5 air pollutants concentrations, existing monitoring stations are seen to be clustered mostly by geographical locations of the eight zones in Seoul. And the stations included in the same cluster are redundantly monitoring air pollutants exhibiting similar atmospheric behavior, thus it can be seen that they are being operated inefficiently. Because monitoring stations groups representing redudancy were different depending on measurement items and several pollutants are being measured at the same time in each air monitoring station, it is seemed to be not easy to integrate or transmigrate stations. But it may be proposed as follows : the redundant stations can be integrated or transmigrated based on ozone of which measures are increasing in recent years and alternatively the remaining pollutants other than the pollutant exhibiting similar atmospheric behavior with nearby station's can be measured. So it is considered to be able to operate air quality monitoring networks effectively and economically in order to improve air quality.

Analysis of 20-year air quality trends and relationship with emission data: The case of Florence (Italy)

In EU, a significant percentage of urban population is exposed to pollutant concentrations above limit/target values stated by air quality legislation. In Italy, this percentage is even higher: 71% for PM10 (daily limit value), 58% for O3 (target value), and 45% for NO2 (annual limit value).In this paper a 20-year (1993–2012) air quality analysis in the city of Florence (Italy) is reported based on observations from all stations of urban air quality monitoring network. Main atmospheric pollutants have been examined: SO2, CO, NO, NO2, PM10, and O3. Actually, Florence is affected by serious air quality problems, with NO2, O3 and PM10 limit values regularly exceeded in recent years. Trends of annual pollutant concentrations have been analysed to assess the significance of their long-term pattern. Trends of inventory emission data have been related to concentrations through a linear regressive framework to assess the capability of each emission category to control annual concentration trends.As a result, primary pollutants exhibited a significant decrease, while this was poorly significant for secondary species despite remarkable reduction in their precursors emissions. A major role is played by meteorological conditions strongly unfavourable to pollutant dispersion, along with vehicle fleet increase and variation over the years.

An ambient air quality monitoring network for Buenos Aires city

This paper proposes an objective methodology for designing a multi-pollutant urban air-quality monitoring network to detect concentrations greater than reference concentration levels (CL). The network design includes both air quality simulation and an objective analysis based on monitoring tasks. The proposed methodology is applied to the city of Buenos Aires. Pollutants of concern are nitrogen oxides, carbon monoxide and PM10. Considering CL as 50% of the Air Quality Standards, the design methodology determines six monitoring sites where to measure different pollutants at the same place. 'Spatial representativeness' of detected exceedances is evaluated analysing air quality simulations of cases with C > CL at any monitoring site and obtaining the areas in the city where concentrations are expected to exceed CL with 70%, 80% and 90% of probability.

Statistical and diagnostic evaluation of the ADMS-Urban model compared with an urban air quality monitoring network

The present study examines the behaviour of the ADMS-Urban air quality forecasting model in predicting dispersion of traffic-related pollutants in urban areas. The study has been carried out in Ravenna (NE Italy), a medium-sized town where pollution produced by vehicle traffic accounts for most of the emissions. ADMS-Urban performances have been assessed through statistical analysis, by comparing carbon monoxide concentrations (vehicle traffic tracing pollutant) estimated by the model with concentrations measured by stations of the air quality monitoring network. Although the correspondence of values estimated by ADMS-Urban with measured values turns out to be satisfactory, the study shows that the model tends to produce an underestimated value compared with the actual situation, and identifies a corrective method that makes it possible to improve the relevant performances. Furthermore, the diagnostic analysis highlights that the model performances depend upon some meteorological parameters.

Design of an Air-Quality Surveillance System for Buenos Aires City Integrated by a NO x Monitoring Network and Atmospheric Dispersion Models

This paper presents an air-quality surveillance system designed to detect the occurrence of air pollutant concentrations greater than a reference level in an urban area. The system is integrated by an air-quality monitoring network and atmospheric dispersion models simulations. An objective methodology to design an urban air-quality monitoring network is proposed. This methodology is based on the analysis of air-quality modelling results. The procedure is applied to design an air-quality monitoring network to control NOx concentration levels in Buenos Aires City. Results indicate that six monitors will detect the occurrence of concentration greater than the air-quality guidelines with an efficiency of about 67%. Once a violation is detected, results of atmospheric dispersion models will help in the determination of affected areas. Four possible examples are included to illustrate the assistance that the results of atmospheric dispersion models can bring to a better estimation of possible affected areas in the city. Combining these results with the last census data, an estimation of the inhabitants possibly exposed is obtained.

Exposure to ultrafine particles from traffic in city streets and the urban atmosphere

Mass-based emission controls are successfully reducing pollutant levels of fine particles from road vehicles, but may have actually increased the emission of ultrafine particles and their persistence in the atmosphere just as growing evidence indicates that these ultrafine particles present the greatest threat to health upon inhalation. These particles have so little mass that they barely register in measurements made by current urban air quality monitoring networks, which measure mass of particulate matter (PM-sub-10). Current daily/hourly monitoring of PM-sub-10 for the purposes of air quality management fails to represent the wide variation and episodicity in exposure of an urban population to the threat from traffic-sourced ultrafine particles. In order to quantify and interpret this variability, data from experiments employing sophisticated, high-resolution instrumentation in U.K. cities is offered. This data illustrates the variability on time scales from minutes to hours of ultrafine particle exposure on busy streets, and shows how meteorological factors and urban topography determine the exposure to traffic particle emissions in the surrounding urban environment. Such information has key consequences for the assessment of future emission reduction and air quality improvement strategies, especially localized ones.

Comparison of model predictions with the data of an urban air quality monitoring network in Izmir, Turkey

The CALMET meteorological model and its puff dispersion model CALPUFF were used to predict dispersion of the sulfur dioxide emissions from industrial and domestic heating sources in Izmir, the third biggest province in Turkey. The modeling domain covered an area of 80×100 km centered at the metropolitan area of Izmir with grid spacing of 1000 m. Statistical analyses were carried out to evaluate the model performance by comparing the predicted and measured time series of sulfur dioxide concentrations at four monitoring stations using two main methods: root of the mean square error (RMSE) and an index of agreement (d). The index of agreement varied from 0.51 to 0.77 at four monitoring stations and the total RMSE ranged from 0.36 to 0.66 for the year 2000. The overall model performance for four monitoring stations was found good with an accuracy of about 68%. The agreement of model predictions and measurements was better for two urban monitoring stations (Karsiyaka and Bornova), compared with the other urban stations (Alsancak and Konak).

Statistical and diagnostic evaluation of a new-generation urban dispersion modelling system against an extensive dataset in the Helsinki area

We have developed a modelling system for predicting the traffic volumes, emissions from stationary and vehicular sources, and atmospheric dispersion of pollution in an urban area. This paper describes a comparison of the NO x and NO 2 concentrations predicted using this modelling system with the results of an urban air quality monitoring network. We performed a statistical analysis to determine the agreement between predicted and measured hourly time series of concentrations at four permanently located and three mobile monitoring stations in the Helsinki Metropolitan Area in 1996–1997 (at a total of ten urban and suburban measurement locations). At the stations considered, the so-called index of agreement values of the predicted and measured time series of the NO 2 concentrations vary between 0.65 and 0.82, while the fractional bias values range from −0.29 to +0.26. In comparison with corresponding results presented in the literature, the agreement between the measured and predicted datasets is good, as indicated by these statistical parameters. The seasonal variations of the NO 2 concentrations were analysed in terms of the relevant meteorological parameters. We also analysed the difference between model predictions and measured data diagnostically, in terms of meteorological parameters, including wind speed and direction (the latter separately for two wind speed classes), atmospheric stability and ambient temperature, at two monitoring stations in central Helsinki. The modelling system tends to overpredict the measured NO 2 concentrations both at the highest ( u⩾6 m s −1) and at the lowest wind speeds ( u<2 m s −1). For higher wind speeds, the modelling system overpredicts the measured NO 2 concentrations in certain wind direction intervals; specific ranges were found for both monitoring stations considered. The modelling system tends to underpredict the measured concentrations in convective atmospheric conditions, and overpredict in stable conditions. The possible physico-chemical reasons for these differences are discussed.

A modelling system for predicting urban air pollution:: comparison of model predictions with the data of an urban measurement network in Helsinki

We have developed a modelling system for predicting the traffic volumes, emissions from stationary and vehicular sources, and atmospheric dispersion of pollution in an urban area. A companion paper addresses model development and its applications. This paper describes a comparison of the predicted NO x and NO 2 concentrations with the results of an urban air quality monitoring network. We performed a statistical analysis concerning the agreement of the predicted and measured hourly time series of concentrations, at four monitoring stations in the Helsinki metropolitan area in 1993. The predicted and measured NO 2 concentrations agreed well at all the stations considered. The agreement of model predictions and measurements for NO x and NO 2 was better for the two suburban monitoring stations, compared with the two urban stations, located in downtown Helsinki.