International Business Machines Corporation

Xi Xia

Xin Xin Bai

Xiao Guang Rui

Wei Zhao

Wen Jun Yin

Junmei Qu

Lingyun Wang

Jin Yan Shao

Jin Dong

Yu Xin Zhao

Hui Du

Xin Jie Lv

Bao Guo Xie

Jian Yao

Hai Feng Wang

Chao Zhang

Ling Wang

Meng Zhang

Ya Ming Zhang

A method, a device and a computer program product for abnormal air pollution emission prediction are proposed. In the method, a first set of features characterizing air condition in a zone is obtained. Whether the zone is subject to abnormal air pollution emission in a future first time period is determined based on the first set of features and using a first prediction classifier. In response to determining that the zone is subject to abnormal air pollution emission in the first time period, a second set of features characterizing air condition in the zone is obtained. A future second time period in which the zone is subject to abnormal air pollution emission is determined based on the second set of features and using a second prediction classifier. The second time period is included in the first time period. In this way, the abnormal air pollution emission in the zone can be accurately and efficiently predicted.

Abnormal air pollution emission prediction

An abnormal area is detected using an initial spatial weights matrix between pairs of air quality sensors in a plurality of air quality sensors distributed across a geographical area and air quality data for each air quality sensor. The spatial weights matrix utilizes a distance between pairs of air quality sensors and wind direction through the geographical area. The initial spatial weights matrix and air quality data are used to calculate a plurality of local moran's indexes, one for each air quality sensor. The plurality of local moran's indexes are used to divide the plurality of air quality sensors into four groups. The groups are classified as proper or improper, and the proper groups are identified as abnormal areas.

Detection of area of abnormal air quality within a geographical area

Correction management techniques are provided. In one embodiment, the techniques involve determining, via a first machine learning model, a first and second data based on a respective first and second raw data obtained from a plurality of sensors, determining, based on a deviation between the first data and the second data, an inaccuracy of the first data, identifying an ambient situation corresponding to the first raw data and the second raw data, selecting, from historical raw data of the plurality of sensors, a group of raw data corresponding to the ambient situation, and correcting, via a second machine learning model, the first data based on the selected group of raw data.

Error correction

In an embodiment of the present disclosure, a method for modeling prediction of inhalable particles concentration is disclosed. In the method, at least one dispersal event is identified, and at least one accumulation event is identified based on the identified at least one dispersal event. Then a dispersal prediction model is generated based on the identified at least one dispersal event. Then at least one accumulation level of inhalable particles concentration is obtained for the at least one accumulation event. A change prediction model for the accumulation level is generated. Then a plurality of accumulation prediction models is generated.

Prediction of inhalable particles concentration

A method for tracking and identifying a polluted air mass's transmission trajectory in real 3-D space. In one aspect, a polluted air mass's transmission path identification is based on a monitoring of PM2.5 concentration in cubic volumes of an air mass. The method computes a transmission path of polluted air that considers wind-pressure conversion, the displacement estimation with mass concentration, and planetary boundary layer (PBLP height constraint) for 3-D cubic grids. The resultant determination of a polluted air mass's transmission trajectory in real 3-D space generates more practical and reliable results for intensive knowledge of the transport pathways and potential pollution sources in real 3-D space.

3-D air pollution transmission path identification

An embodiment of the invention provides a method where an air quality forecast having an air quality turning point is received. A processor identifies an updated air quality turning point based on weather observation information. An air quality forecast at the updated air quality turning point is generated with the weather observation information and human input, which can include human knowledge from a weather expert or an air quality expert. An air quality forecast for a first time period directly prior to the updated air quality turning point is generated with the air quality forecast at the updated air quality turning point and the human input. An air quality forecast for a second time period directly after the updated air quality turning point is generated with the air quality forecast for the first time period, the air quality forecast at the updated air quality turning point, and the human input.

Integrated air quality forecasting

A system, method and computer program product for tracking and identifying a polluted air mass's transmission trajectory in real 3-D space. In one aspect, a polluted air mass's transmission path identification is based on a monitoring of PM2.5 concentration in cubic volumes of an air mass. The method computes a transmission path of polluted air that considers wind-pressure conversion, the displacement estimation with mass concentration, and planetary boundary layer (PBLP height constraint) for 3-D cubic grids. The resultant determination of a polluted air mass's transmission trajectory in real 3-D space generates more practical and reliable results for intensive knowledge of the transport pathways and potential pollution sources in real 3-D space.

A method and a device for predicting insulator pollution grade includes acquiring prediction data affecting the insulator pollution grade; acquiring current pollution status of the insulator; predicting the insulator pollution grade based on the prediction data, the current pollution status and an insulator pollution grade calculating model, wherein the insulator pollution grade calculating model at least comprises an initial pollution status variable of the insulator, and a pollutant accumulation prediction and a pollutant reduction prediction based on the initial pollution status variable, at least one of the accumulation prediction and the reduction prediction being associated with the prediction data, and the initial pollution status variable being associated with the current pollution status.

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Beijing, China

Xi Xia