Forecasting Air Quality Indicators for 33 Cities in China

Wu, Lifeng; Zhao, Hongying

doi:10.1002/clen.201900097

Cited by 7 publications

(3 citation statements)

References 29 publications

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“…Because there is limited data available, as an alternative, one can also use the grey system model based on the linear differential equation. [ 60 ] Until we found the ANN model able to show the fluctuation, which cannot be achieved in the ordinary differential equation. Consequently, it leads to a higher error value when we apply ANN.…”

Section: Resultsmentioning

confidence: 99%

Assessment of the Present State and Future Fate of River Saraswati, India: Water Quality Indices and Forecast Models as Diagnostic and Management Tools

Pramanik

Kaviraj

Saha

2023

CLEAN Soil Air Water

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Water quality assessment is key to the conservation and management of rivers. River Saraswati, a distributary of the river Ganga, serves as a lifeline to many villages in the district Hooghly in West Bengal, India. As the river is gradually dying due to diverse man‐made pollution, ten water quality parameters in two sampling spots (PR‐1 and PR‐2) in the river are monitored month‐wise from March 2017 to February 2020, and these are compared with those from a reference pond. The water quality index (WQI) is determined for the two riverine spots and the reference pond based on the Canadian Council of Ministers of Environment WQI (CCMEWQI) and weighted arithmetic WQI, respectively. In addition to actual observations, three different forecasting methods, exponential smoothing, autoregressive integrated moving average, and artificial neural network, are used to predict WQI for the next two years. This study indicates that free CO2, dissolved oxygen, and turbidity are the key parameters to evaluate this river's anthropogenic stress and health. The actual and forecasted results reflect the precipitous degradation of CCMEWQI in PR‐2. Therefore, the immediate intervention of all stakeholders is required to adopt an integrated and comprehensive river management plan to save the river from utter obliteration.

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Section: Resultsmentioning

confidence: 99%

Assessment of the Present State and Future Fate of River Saraswati, India: Water Quality Indices and Forecast Models as Diagnostic and Management Tools

Pramanik

Kaviraj

Saha

2023

CLEAN Soil Air Water

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“…On this basis, the fractional accumulative gray forecast model (FGM(1,1)) was used to forecast the primary pollutants in 27 Chinese provinces from 2022 to 2026. The problems caused by air pollution have attracted much attention in recent years, as well as concentrations of different pollutants adopting China's ambient air quality standards [19].…”

Section: Data Sourcesmentioning

confidence: 99%

Chinese Provincial Air Pollutant Concentration Prediction over the Long Term

Zhao

2023

Atmosphere

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Although China’s urban air quality has improved, there are still many cities that do not meet China’s ambient air quality standards and experience serious air pollution problems, causing tremendous damage to people’s health and sustainable social development. For the sake of obtaining the specific time when China’s ambient air quality will reach the standard, the annual mean air pollutant concentrations of 27 Chinese provinces are predicted and analyzed. Based on original data from air pollutant concentrations in 27 Chinese provinces from 2017 to 2021, a gray prediction model with fractional order accumulation is established to analyze and predict the concentration of pollutants in 27 provinces. The applicability of the model is then validated by mean absolute percentage error values. According to the forecast results, by 2026, the concentrations of six pollutants, PM2.5, PM10, SO2, NO2, CO, and O3, will all meet Class II air quality standards in 25 Chinese provinces, namely Beijing, Chongqing, Shanghai, Hebei, Jiangsu, Shanxi, Zhejiang, Liaoning, Anhui, Jilin, Fujian, Heilongjiang, Jiangxi, Hubei, Qinghai, Hunan, Guangdong, Hainan, Guangxi, Guizhou, Shaanxi, Gansu, Yunnan, Inner Mongolia, and Xinjiang (corrected for the effect of sandstorms). Tianjin, Sichuan, and Xinjiang (not corrected for the effect of sandstorms) still exceed the standard in the annual mean concentration of PM2.5, NO2, and PM10, respectively. Sichuan and Tianjin are, respectively, expected to meet Class II air quality standards in 2027 and 2030, and Xinjiang (not corrected for the effect of sandstorms) is expected to fail to meet Class II standards in the next 15 years. Finally, the current situation with respect to China’s ambient air quality in 27 Chinese provinces is analyzed, and corresponding suggestions are put forward to offer an explicit direction for relevant departments.

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“…Wu and his colleagues [ 21 ] successfully applied fractional accumulation to fuel production in China. Since the development of these studies, scholars have conducted in-depth research in the field of fractional-order grey models and achieved remarkable results [ 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. FAGM(1,1) is the most widely used grey model at present.…”

Section: Introductionmentioning

confidence: 99%

A Novel Fractional Accumulative Grey Model with GA-PSO Optimizer and Its Application

Huang

2023

Sensors

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The prediction of cyber security situation plays an important role in early warning against cyber security attacks. The first-order accumulative grey model has achieved remarkable results in many prediction scenarios. Since recent events have a greater impact on future decisions, new information should be given more weight. The disadvantage of first-order accumulative grey models is that with the first-order accumulative method, equal weight is given to the original data. In this paper, a fractional-order cumulative grey model (FAGM) is used to establish the prediction model, and an intelligent optimization algorithm known as particle swarm optimization (PSO) combined with a genetic algorithm (GA) is used to determine the optimal order. The model discussed in this paper is used for the prediction of Internet cyber security situations. The results of a comparison with the traditional grey model GM(1,1), the grey model GM(1,n), and the fractional discrete grey seasonal model FDGSM(1,1) show that our model is suitable for cases with insufficient data and irregular sample sizes, and the prediction accuracy and stability of the model are better than those of the other three models.

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Forecasting Air Quality Indicators for 33 Cities in China

Cited by 7 publications

References 29 publications

Assessment of the Present State and Future Fate of River Saraswati, India: Water Quality Indices and Forecast Models as Diagnostic and Management Tools

Assessment of the Present State and Future Fate of River Saraswati, India: Water Quality Indices and Forecast Models as Diagnostic and Management Tools

Chinese Provincial Air Pollutant Concentration Prediction over the Long Term

A Novel Fractional Accumulative Grey Model with GA-PSO Optimizer and Its Application

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