A Six-year long (2013–2018) High-resolution Air Quality Reanalysis Dataset over China base on the assimilation of surface observations from CNEMC

Abstract. Air pollution in China has changed substantially since 2013, and the effects such changes bring to the human health and environment has been an increasingly hot topic in many scientific fields. Such studies, however, are often hindered by a lack of long-term air quality dataset in China of high accuracy and spatiotemporal resolutions. In this study, a six-year long high-resolution Chinese air quality reanalysis datasets (CAQRA) has been developed by assimilating over 1000 surface air quality monitoring sites from China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). Surface fields of six conventional air pollutants in China, namely PM2.5, PM10, SO2, NO2, CO and O3 for period 2013–2018, are provided at high spatial (15 km ×15 km) and temporal (1 hour) resolutions. This paper aims to document this dataset by providing the detailed descriptions of the assimilation system and presenting the first validation results for the reanalysis dataset. A five-fold cross validation (CV) method was used to assess the quality of CAQRA. The CV results show that the CAQRA has excellent performances in reproducing the magnitude and variability of the surface air pollutants in China (CV R2 = 0.52–0.81, CV RMSE = 0.54 mg/m3 for CO and 16.4–39.3 μg/m3 for other pollutants at the hourly scale). The interannual changes of the air quality in China were also well represented by CAQRA. Through the comparisons with the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) produced by the European Centre for Medium-Range Weather Forecasts (ECWMF) based on assimilating satellite products, we show that the CAQRA has higher accuracy in representing the surface gaseous air pollutants in China due to the assimilation of surface observations. The finer horizontal resolution of CAQRA also makes it more suitable for the air quality studies in the regional scale. We further validate the PM2.5 reanalysis dataset against the independent datasets from the U.S. Department of State Air Quality Monitoring Program over China, and the accuracy of PM2.5 reanalysis was also compared to that of the satellite estimated PM2.5 concentrations. The results indicate that the PM2.5 reanalysis shows good agreement with the independent observations (R2 = 0.74–0.86, RMSE = 16.8–33.6 μg/m3 in different cities) and its accuracy is higher than most satellite estimates. This dataset would be the first high-resolution air quality reanalysis dataset in China that can simultaneously provide the surface concentrations of six conventional air pollutants in China, which should be of great value for many studies, such as the assessment of health impacts of air pollution, investigation of the changes of air quality in China and providing training data for the statistical or AI (Artificial Intelligence) based forecast. The whole datasets are freely available at: https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a teaser product which contains the monthly and annual mean of the CAQRA has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the potential users to download and to evaluate the improvement of CAQRA.

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A 6-year-long (2013–2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC

Earth System Science Data ◽

10.5194/essd-13-529-2021 ◽

2021 ◽

Vol 13 (2) ◽

pp. 529-570

Author(s):

Lei Kong ◽

Xiao Tang ◽

Jiang Zhu ◽

Zifa Wang ◽

Jianjun Li ◽

...

Keyword(s):

Air Quality ◽

High Resolution ◽

Air Pollutants ◽

Regional Scale ◽

Monitoring Program ◽

Health Impact ◽

Horizontal Resolution ◽

Training Data ◽

Reanalysis Dataset ◽

Surface Observations

Abstract. A 6-year-long high-resolution Chinese air quality reanalysis (CAQRA) dataset is presented in this study obtained from the assimilation of surface observations from the China National Environmental Monitoring Centre (CNEMC) using the ensemble Kalman filter (EnKF) and Nested Air Quality Prediction Modeling System (NAQPMS).This dataset contains surface fields of six conventional air pollutants in China (i.e. PM2.5, PM10, SO2, NO2, CO, and O3) for the period 2013–2018 at high spatial (15 km×15 km) and temporal (1 h) resolutions. This paper aims to document this dataset by providing detailed descriptions of the assimilation system and the first validation results for the above reanalysis dataset. The 5-fold cross-validation (CV) method is adopted to demonstrate the quality of the reanalysis. The CV results show that the CAQRA yields an excellent performance in reproducing the magnitude and variability of surface air pollutants in China from 2013 to 2018 (CV R2=0.52–0.81, CV root mean square error (RMSE) =0.54 mg/m3 for CO, and CV RMSE =16.4–39.3 µg/m3 for the other pollutants on an hourly scale). Through comparison to the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECWMF), we show that CAQRA attains a high accuracy in representing surface gaseous air pollutants in China due to the assimilation of surface observations. The fine horizontal resolution of CAQRA also makes it more suitable for air quality studies on a regional scale. The PM2.5 reanalysis dataset is further validated against the independent datasets from the US Department of State Air Quality Monitoring Program over China, which exhibits a good agreement with the independent observations (R2=0.74–0.86 and RMSE =16.8–33.6 µg/m3 in different cities). Furthermore, through the comparison to satellite-estimated PM2.5 concentrations, we show that the accuracy of the PM2.5 reanalysis is higher than that of most satellite estimates. The CAQRA is the first high-resolution air quality reanalysis dataset in China that simultaneously provides the surface concentrations of six conventional air pollutants, which is of great value for many studies, such as health impact assessment of air pollution, investigation of air quality changes in China, model evaluation and satellite calibration, optimization of monitoring sites, and provision of training data for statistical or artificial intelligence (AI)-based forecasting. All datasets are freely available at https://doi.org/10.11922/sciencedb.00053 (Tang et al., 2020a), and a prototype product containing the monthly and annual means of the CAQRA dataset has also been released at https://doi.org/10.11922/sciencedb.00092 (Tang et al., 2020b) to facilitate the evaluation of the CAQRA dataset by potential users.

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Pollution Weather Prediction System: Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living

Sensors ◽

10.3390/s20185448 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5448 ◽

Cited By ~ 2

Author(s):

Sharnil Pandya ◽

Hemant Ghayvat ◽

Anirban Sur ◽

Muhammad Awais ◽

Ketan Kotecha ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Linear Regression ◽

Air Pollutants ◽

Weather Prediction ◽

Quality Monitoring ◽

Prediction System ◽

Air Quality Monitoring ◽

Air Pollution Prediction ◽

Pollution Prediction

Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.

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Developing High-resolution Air Quality Reanalysis Dataset over China for Years 2013-2018 Based on Ensemble Kalman Filter and Surface Observations from CNEMC

10.5194/egusphere-egu2020-6848 ◽

2020 ◽

Author(s):

Lei Kong ◽

Xiao Tang ◽

Jiang Zhu ◽

Zifa Wang ◽

Huangjian Wu ◽

...

Keyword(s):

Kalman Filter ◽

Air Quality ◽

High Resolution ◽

Ensemble Kalman Filter ◽

Air Pollutants ◽

Reanalysis Data ◽

Training Data ◽

Reanalysis Dataset ◽

Air Quality Prediction ◽

Surface Observations

A six-year long high-resolution Chinese air quality reanalysis datasets (CAQRA) covering the period 2013-2018 has been developed in this study by assimilating over 1000 surface air quality monitoring sites from China National Environmental Monitoring Centre (CNEMC) based on the ensemble Kalman filter (EnKF) and the Nested Air Quality Prediction Modeling System (NAQPMS). This reanalysis provides the surface fields of six conventional air pollutants in China, namely PM2.5, PM10, SO2, NO2, CO and O3, at high spatial (15km&#215;15km) and temporal (1 hour) resolutions. This paper aims to document this dataset by providing the detailed descriptions of the assimilation system and presenting the first validation results for the reanalysis fields of air pollutants in China. A twenty-fold cross validation (CV) method was used to assess the quality of CAQRA. The CV results show that the CAQRA has excellent performances in reproducing the magnitude and variability of the air pollutants in China with the biases (normalized mean bias) of the reanalysis data about -2.6 (-4.9%) &#956;g/m3 for PM2.5, -6.8 (-7.6%) &#956;g/m3 for PM10, -2.0 (-8.5%) &#956;g/m3 for SO2, -2.3 (-6.9%) &#956;g/m3 for NO2, -0.06 (-6.1%) mg/m3 for CO and -2.3 (-4.0%) &#956;g/m3 for O3. The interannual changes of the air quality in China were also well represented by the CAQRA in terms of the six air pollutants. Comparisons with previous datasets of daily PM2.5, SO2 and NO2 concentrations indicate that the CAQRA is more accurate with smaller RMSE values. We also compared our reanalysis dataset to the CAMSRA (The Copernicus Atmosphere Monitoring Service reanalysis) produced by ECMWF (European Centre for Medium-Range Weather Forecasts), which suggests that the CAQRA has higher accuracy in representing the surface air pollutants in China due to the assimilation of surface observations. This reanalysis dataset can provide us comprehensive pictures of the air quality in China from 2013 to 2018 with a complete spatial and temporal coverage, which can be used in the assessment of health impacts of air pollution, validation of model simulations and providing training data for the statistical or AI (Artificial Intelligence) based forecast.

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Methodological Approaches to Optimizing Ambient Air Quality Monitoring Programs within the Framework of the Federal Clean Air Project (on the example of Nizhny Tagil)

ЗДОРОВЬЕ НАСЕЛЕНИЯ И СРЕДА ОБИТАНИЯ - ЗНиСО / PUBLIC HEALTH AND LIFE ENVIRONMENT ◽

10.35627/2219-5238/2020-330-9-38-47 ◽

2020 ◽

pp. 38-47

Author(s):

VB Gurvich ◽

DN Kozlovskikh ◽

IA Vlasov ◽

IV Chistyakova ◽

SV Yarushin ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Health Risks ◽

Monitoring Program ◽

Ambient Air ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Ambient Air Quality ◽

Clean Air ◽

Methodological Approaches

ntroduction: One of the key socially significant results of implementing the Federal Clean Air Project is the maximum possible mitigation of inhalation health risks by achieving the target rate of reducing emissions (by 20 % against the level of 2017) in a number of cities included in the federal project as priority areas. Materials and methods: Ambient air pollution monitoring as a measure of this accomplishment is indispensable both for verification of applying the model to estimating surface concentrations of pollutants, assessing health risks, and evaluating changes in ambient air quality. For the objectivity of such assessments, it is fundamental to determine the list of priority health-threatening air pollutants, to select monitoring sites that best characterize population exposure to these chemicals, and to plan air quality monitoring programs setting sampling frequency and volume. Results: The article presents the results of implementing methodological approaches adopted by the Russian Federal Service for Surveillance in the Sphere of Consumer Rights and Human Wellbeing (Rospotrebnadzor) to optimize ambient air quality monitoring within the framework of solving the tasks of the Federal Clean Air Project in the city of Nizhny Tagil, Sverdlovsk Region, in 2019. The Nizhny Tagil air quality monitoring program for 2020 has been developed and tested. This program, in conjunction with similar programs carried out by the Russian Federal Hydrometeorology and Environmental Monitoring Service (Roshydromet) and the Ministry of Natural Resources and Environment of the Sverdlovsk Region and taking into account their implementation over the past five years, helps provide implementers of the federal project with air pollution data to address its key challenges. Conclusions: The adopted ambient air quality monitoring program implemented in Nizhny Tagil in 2020 by the Center for Hygiene and Epidemiology in the Sverdlovsk Region meets terms and requirements of the Federal Clean Air Project.

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RESEARCH ON AIR POLLUTANTS NO2 AND SO2 IN RASEINIAI AREA USING PASSIVE SORBENTS AND BIO INDICATIVE METHODS / RASEINIŲ RAJONO ORO UŽTERŠTUMO NO2 IR SO2 TYRIMAI, TAIKANT PASYVIŲJŲ SORBENTŲ BEI BIOINDIKACINIUS METODUS

Mokslas - Lietuvos ateitis ◽

10.3846/mla.2011.085 ◽

2011 ◽

Vol 3 (5) ◽

pp. 43-49 ◽

Cited By ~ 1

Author(s):

Neringa Mikelsonaitė ◽

Agnė Kazlauskienė

Keyword(s):

Air Pollution ◽

Air Quality ◽

Environmental Protection ◽

Research Methods ◽

Air Pollutants ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Maple Leaves ◽

Pollution Research

The carried out research analyzes air pollution in Raseiniai area. SO2 and NO2 exhaustion analysis is based on data for the period 2006–2009 provided by Kaunas Environmental protection department. Two air pollution research methods based on passive sorbents and bio indicators are proposed. The amount of SO2 and NO2 air pollutants is determined by conducting moss (Lichenes) tests and analyzing the fungus of maple leaves (Rhytisma acerinum). The performed research also describes the methods applied for analysis. Initial air quality monitoring using passive sorbents was carried out in the autumn of 2010. The primary results of this research revealed that the amount of analyzed air pollutants was below the permissible norm. In order to get reliable and objective results, research will be continued in the spring and summer of 2011. Santrauka Nagrinėjama oro taršos problema Raseinių rajone. Remiantis Kauno regiono aplinkos apsaugos departamento Raseinių rajono agentūros duomenimis, buvo tiriama 2006–2009 m. laikotarpio SO2 ir NO2 išmetimų kitimo tendencija. Pasiūlyti du oro taršos SO2 ir NO2 tyrimo metodai: pasyviųjų sorbentų ir boindikacijos. Tiriant bioindikacijos metodu, siūloma oro užterštumą SO2 ir NO2 teršalais vertinti atliekant kerpių (Lichenes) testą ir tiriant klevų lapų grybą (Rhytisma acerinum). Aprašyta siūlomų metodų atlikimo tvarka. Pirminiai oro kokybės stebėjimai buvo atlikti 2010 m. rudens sezonu, naudojant pasyviuosius sorbentus. Pirminiai rezultatai parodė, kad tirti teršalai neviršijo žmonių apsaugai nustatytų ribinių verčių. Pradėtus tyrimus tikslinga tęsti per 2011 m. pavasario ir vasaros sezonus, siekiant gauti objektyvius ir išsamius oro kokybės tyrimų duomenis Raseinių rajono teritorijoje.

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Influences of Season and Geography on Urban Air Quality Monitoring Stations (AQMS) Representativeness Areas by High Resolution Air Quality Monitoring

10.5194/egusphere-egu2020-13652 ◽

2020 ◽

Author(s):

Tilman Leo Hohenberger

Keyword(s):

Air Pollution ◽

Hong Kong ◽

Air Quality ◽

High Resolution ◽

Urban Air Pollution ◽

Quality Monitoring ◽

Urban Air ◽

Air Quality Monitoring ◽

Air Quality Monitoring Stations ◽

Better Than

Urban air pollution remains a key pressure on public health. With the megatrend of urbanization and its forcing on emissions and exposure, effective monitoring tools in cities are at the center of prevention efforts.Air Quality Monitoring Stations (AQMS) are traditionally used for regulatory efforts and, increasingly, as publicly available information sources. Facing high levels of air pollution heterogeneity in complex urban environments, a simple spatial approach is often misleading when choosing an AQMS that represents local street-level conditions the best. Model-based calculation of representativeness areas are rare for the urban scale (e.g. Rodriguez et al., 2019), and suffer from short model times, low model correlations and a lack of external validation by observation data. Moreover, as both health impacts and air-pollution episodes are influenced by environmental factors, the sensitivity of representativeness areas to wind impacts and during different seasons are a further point of interest not covered well by previous literature.For the high-density environment of geographically complex Hong Kong, we used a full year (2019) of high-resolution air quality modelling (ADMS-Urban) data to establish representativeness areas for the territory&#8217;s 16 AQMS. We constructed representativeness areas for key air-pollutants for the full period and based on season and wind speed. We parameterized the effects of wind and geography on the size and shape of the representativeness areas. Furthermore, we validated our findings by a series of week-long outdoor measurements aimed to cover the whole territory of Hong Kong.Our results show that Hong Kong&#8217;s AQMS network covering the territory well for a PM2.5, PM10 and O3, where the mean CSF (hourly Concentration Similarity Frequency with a target of &#177;20%) of each grid-cell to the best matching AQMS lies at around 60%. Both NO2 and SO2 are less well represented, with a CSF of around 30%. Moreover, we show that representativeness areas calculated from similarity-based metrices as CSF and percentage difference represent the impact of geographical features on pollution dispersion better than correlation-based metrices (R2 and ioa). It was further found that AQMS represent upwind areas better than downwind areas, especially in areas exposed to open wind-flow, and that the represented areas change strongly over the course of a year.In this study, we showcase the ability of high-resolution urban air-pollution modelling to guide the public with information on AQMS representativeness. Furthermore, we report that representativeness areas are non-static, changing with seasons and under the influence of wind. High-resolution urban modelling can further be used to gauge the quality of AQMS networks and assess the need and location of additions to an existing network.&#160;Rodriguez, D., Valari, M., Payan, S., & Eymard, L. (2019). On the spatial representativeness of NOX and PM10 monitoring-sites in Paris, France. Atmospheric Environment: X, 1, 100010.

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A Study Regarding the Air Pollution in Cluj-Napoca City

Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca Agriculture ◽

10.15835/buasvmcn-agr:6527 ◽

2011 ◽

Vol 68 (2) ◽

Author(s):

Ilarie IVAN ◽

Teodora DEAC

Keyword(s):

Air Pollution ◽

Air Quality ◽

Air Pollutants ◽

Quality Monitoring ◽

Monitoring Station ◽

City Management ◽

Air Quality Monitoring ◽

Parameter Values ◽

Modern Apparatus

In this paper, a study regarding the air pollution in Cluj-Napoca City has been conducted. Thus, the SO2, NOx, NO and NO2 parameter values were monitored using modern apparatus of air quality monitoring station, located in Cluj-Napoca city. The values were measured daily at 14 o’clock during March, 2011. Using the analyzed data, the maximum and minimum limits of air pollutants were quantified. Thus, it was assessed that the pollution is higher from Monday till Friday and it decreases during Saturday and Sunday. According to the analyzed data, a series of proposals to Cluj-Napoca city management were elaborated in order to reduce air pollution.

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Contribution of Local and Transboundary Air Pollution to the Urban Air Quality of Fukuoka, Japan

Atmosphere ◽

10.3390/atmos12040431 ◽

2021 ◽

Vol 12 (4) ◽

pp. 431

Author(s):

Ayako Yoshino ◽

Akinori Takami ◽

Keiichiro Hara ◽

Chiharu Nishita-Hara ◽

Masahiko Hayashi ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Urban Areas ◽

Air Pollutants ◽

Regional Scale ◽

Gaseous Species ◽

Transboundary Air Pollution ◽

Local Air Pollution ◽

The City

Transboundary air pollution (TAP) and local air pollution (LAP) influence the air quality of urban areas. Fukuoka, located on the west side of Japan and affected by TAP from the Asian continent, is a unique example for understanding the contribution of LAP and TAP. Gaseous species and particulate matter (PM) were measured for approximately three weeks in Fukuoka in the winter of 2018. We classified two distinctive periods, LAP and TAP, based on wind speed. The classification was supported by variations in the concentration of gaseous species and by backward trajectories. Most air pollutants, including NOx and PM, were high in the LAP period and low in the TAP period. However, ozone was the exception. Therefore, our findings suggest that reducing local emissions is necessary. Ozone was higher in the TAP period, and the variation in ozone concentration was relatively small, indicating that ozone was produced outside of the city and transported to Fukuoka. Thus, air pollutants must also be reduced at a regional scale, including in China.

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Domain adaptation based deep calibration of low-cost PM2.5 sensors

10.36227/techrxiv.15276186 ◽

2021 ◽

Author(s):

Sonu Kumar Jha ◽

Mohit Kumar ◽

Vipul Arora ◽

Sachchida Nand Tripathi ◽

Vidyanand Motiram Motghare ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Domain Adaptation ◽

Low Cost ◽

Monitoring Network ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Proposed Model ◽

Temperature And Humidity ◽

Calibration Approach

<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

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THE ANALYSE OF AIR ENVIRONMENT QUALITY MONITORING IMPROVEMENT POSSIBILITIES

Environment Technology Resources Proceedings of the International Scientific and Practical Conference ◽

10.17770/etr2009vol1.1111 ◽

2015 ◽

Vol 1 ◽

pp. 273

Author(s):

Gotfrīds Noviks ◽

Andris Skromulis

Keyword(s):

Air Quality ◽

Air Pollutants ◽

Quality Evaluation ◽

Quality Monitoring ◽

Dust Particles ◽

Action Program ◽

Air Quality Monitoring ◽

Environment Quality ◽

Air Quality Measurements ◽

Quality Measurements

Paper presents the results of air pollution analyses during last 8 years in Rezekne city. There is carried out a research of atmospheric dust particles, found correlations between concentrations of different air pollutants. Is given overview about air quality measurements in other countries, pointed out air ionization importance on air quality evaluation. The aim of the research – to ground the extension of air quality monitoring indicators including parameters of the air ionisation and to work out an action program to improve an air quality in working areas and recreating zones.

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