Ground-based Hyperspectral Stereoscopic Remote Sensing Network: A Promising Strategy to Learn Coordinated Control of O3 and PM2.5 over China

Joint prevention and control of air pollution (JPCAP) is an inevitable choice for controlling air pollution in the future. The existing air pollution prevention and control methods pay less attention to environmental justice, which results in poor treatment effects; hence, a more reasonable method is imperative. The innovation of this work is to construct a new method for dividing the scope and priorities of JPCAP by introducing environmental justice. First, we analyze the distribution and injustice of air pollution, and then divide JPCAP regions based on environmental justice using a clustering method. Finally, by selecting indicators from the characteristics of air pollution, natural conditions, and trade, the Entropy-TOPSIS method is employed to define the priorities of the JPCAP regions. Accordingly, we divided Beijing, Tianjin, Hebei, and its surrounding areas in China into four JPCAP regions. We also discovered key regions of air pollution control by determining the priorities of the four regions. The new method provides a practical guidance and a theoretical basis for regional joint control of air pollution.

A new method for dividing the scopes and priorities of air pollution control based on environmental justice

Ground-Based Hyperspectral Stereoscopic Remote Sensing Network: A Promising Strategy to Learn Coordinated Control of O3 and PM2.5 over China

As an economically intensive city within the greater bay area (GBA), Shenzhen has suffered from severe ozone pollution in recent years. Numerical modeling is an important tool in daily air quality prediction. However, a combination of factors, such as outdated emission inventories and the uncertainty of weather and numeric models, pose challenges for accurately forecasting pollution periods and levels. Therefore, systematic evaluation of the numerical model performance is crucial for better manual revised prediction of the daily MAD8h and air quality index (AQI). In this paper, a multimodel air quality forecast system, which was built in our public-owned monitoring center station, was evaluated to understand the possibilities of improvements. Based on the 2021 observed data of the national air monitoring station, we utilized a series of metrics to access the predicted air quality index (AQI) and pollution concentration levels from models (including WRF-CMAQ, NAQPMS, and WRF-CHEM) and compared them to the manual prediction. The results indicated that WRF-CMAQ performs outstandingly in comprehensive analysis, with a yearly 24 h (hour) forecast accuracy (A) exceeding 91%. The NAQPMS 24 h forecast presented the lowest accuracy (A = 71%) with a high overprediction rate. The forecast accuracy of WRF-CHEM and NAQPMS in the winter period was 100%. However, frequently changing weather patterns interfered with spring forecasting. Forecasters can accurately predict mild pollution cases (A = 81.8% for 24 h, A = 72.7% for 48 h), whereas numerical models (NAQPMS and WRF-CHEM) showed better sensitivity to moderate pollution cases. For fine particulate evaluation, CMAQ's low biases occurred in winter, and the manually revised air quality forecast (MRAQR) normally gave higher prediction values except in pollution cases. Model underestimation could be explained by missing the long-range dust transport contribution from the northern part of China. The ozone 24 h forecast part revealed that WRF-CMAQ exhibited the highest correlation coefficient (R = 0.70, 19.74% < RMSE < 42.7%), whereas NAQPMS showed relatively poor results with the highest ME (43 μg/m3) and MFE (41.92%). Close examination of synoptic information revealed that ozone pollution cases were subjected to the western Pacific subtropical high (WPSH) system, tropical cycle, and regional strong pollution transport. The NAQPMS ozone overprediction could be explained by the overestimation of troposphere transmission from the surroundings during nonconductive weather patterns. The study investigates a theoretical method for model optimization, which provides solid support for precise emission reduction and targeted pollution control. 

Evaluation of the prediction performance of air quality numerical forecast models in Shenzhen

The direct and indirect radiation forcing of aerosol particles deeply affect the energy budget and the atmospheric chemical and physical processes. To retrieve the vertical aerosol mass fluxes and to investigate the vertical transport process of aerosol by a coherent Doppler lidar (CDL), a practical method for instrumental calibration and aerosol optical properties retrieval based on CDL and sun photometer synchronization observations has been developed. A conversion of aerosol optical properties to aerosol microphysical properties is achieved by applying a well-developed algorithm. Furthermore, combining the vertical velocity measured simultaneously with a CDL, we use the eddy covariance (EC) method to retrieve the vertical turbulent aerosol mass fluxes by a CDL and sun photometer with a spatial resolution of 15 m and a temporal resolution of 1 s throughout the planetary boundary layer (PBL). In this paper, we present a measurement case of 24-h continuous fluxes observations and analyze the diurnal variation of the vertical velocity, the aerosol backscatter coefficient at 1550 nm, the mean aerosol mass concentration, and the vertical aerosol mass fluxes on 13 April 2020. Finally, the main relative errors in aerosol mass flux retrieval, including sample error σF,S, aerosol optical properties retrieval error σF,R, and error introduced from aerosol microphysical properties retrieval algorithm σF,I, are evaluated. The sample error σF,S is the dominating error which increases with height except during 12:00–13:12 LST. The aerosol optical properties retrieval error σF,R is 21% and the error introduced from the aerosol microphysical properties retrieval algorithm σF,I is less than 50%.

Retrieval and Calculation of Vertical Aerosol Mass Fluxes by a Coherent Doppler Lidar and a Sun Photometer

With the increase economic growth and high energy consumption in Shenzhen, pollutant emission increases gradually which leads to air pollution and affect public health. In order to undertake effective measures for preventing and controlling air pollution, it is essential to understand the transmission characteristics of PM2.5 and identify the cause of air pollution in Shenzhen. Based on LIDAR data of Shenzhen, China, the transmission characteristics of PM2.5 across Shenzhen's borders in the winter of 2017 (from December 1, 2017 to February 28, 2018) were analyzed using a four-dimensional flux method. In addition, the sources of PM2.5 in Shenzhen from December 8 to 10, 2017 were analyzed. The results revealed that the East Asian winter monsoon was primarily responsible for the transmission of PM2.5 in Shenzhen, from north to south, and its surrounding areas, and high PM2.5 flux was primarily located at the altitude range of 500–1,500 m. During the monsoon transition period, the surface wind directions changed frequently, which resulted in varying transmission directions of PM2.5 fluxes across Shenzhen. During the period from December 8 to10, PM2.5 was continuously transported to the urban areas of Shenzhen via its northern border due to the influence of strong northerly wind. The PM2.5 fluxes were positive over Songgang station (in the northwestern corner of Shenzhen) and Xiapi station (in the northeastern corner of Shenzhen) from December 8 to 9. The analysis revealed that, the PM2.5 source contribution from regional transmission (representing other land areas within the model domain) reached approximately 50%, whereas the local source contribution was only approximately 10% in Shenzhen.

Integrating LIDAR data and four-dimensional flux method to analyzing the transmission of PM2.5 in Shenzhen

The invention provides an air pollutant prediction method and a device as well as electronic equipment and relates to the technical field of air quality prediction. The air pollutant prediction methodcomprises the following steps: acquiring multiple current prediction results generated by multiple prediction modes, wherein each prediction mode generates one current prediction result; determininga localization parameter corresponding to each prediction mode, so that multiple localization parameters are obtained; according to the multiple localization parameters, optimizing an integrated prediction model; and inputting the multiple current prediction results into the optimized integrated prediction model, so that a collective result is obtained. The air pollutant prediction method providedby the invention can improve accuracy of the collective result.

Air pollutant prediction method and device as well as electronic equipment

Disclosed are an air pollutant forecasting method and apparatus, and an electronic device, which relate to the technical field of air quality forecasting. The method comprises: acquiring a plurality of current forecasting results generated by a plurality of forecasting modes (S110), wherein each forecasting mode generates one current forecasting result; determining a localization parameter corresponding to each forecasting mode to obtain a plurality of localization parameters (S120); according to the plurality of localization parameters, optimizing an integrated forecasting model (S130); and inputting the plurality of current forecasting results into the optimized integrated forecasting model, so as to obtain an aggregation result (S140). The method can improve the accuracy rate of the aggregation result.

Air pollutant forecasting method and apparatus, and electronic device

The invention discloses a data processing method and device, a storage medium and a terminal. The method comprises the steps of deploying a service architecture for performing data processing on datain each air quality numerical mode; inputting input data associated with the current mode information; and running a running script corresponding to the current running mode according to each configuration parameter in the configuration file to obtain a first data set corresponding to the first data capable of running in the first system, and performing data processing on the first data to obtaina second data set corresponding to the second data capable of running in the second system. Therefore, by adoption of the embodiment of the invention, automatic data processing for the data under different operation modes is realized, so that the data processing efficiency under the air quality numerical mode service is improved.

Data processing method and device, storage medium and terminal

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Integrating LIDAR data and four-dimensional flux method to analyzing the transmission of PM2.5 in Shenzhen

Air pollutant prediction method and device as well as electronic equipment

Air pollutant forecasting method and apparatus, and electronic device

Data processing method and device, storage medium and terminal