Air pollution characteristics and their relation to meteorological conditions during 2014–2015 in major Chinese cities

In modern society, air pollution is an important topic as this pollution exerts a critically bad influence on human health and the environment. Among air pollutants, Particulate Matter (PM2.5) consists of suspended particles with a diameter equal to or less than 2.5 μm. Sources of PM2.5 can be coal-fired power generation, smoke, or dusts. These suspended particles in the air can damage the respiratory and cardiovascular systems of the human body, which may further lead to other diseases such as asthma, lung cancer, or cardiovascular diseases. To monitor and estimate the PM2.5 concentration, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) are combined and applied to the PM2.5 forecasting system. To compare the overall performance of each algorithm, four measurement indexes, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) Pearson correlation coefficient and Index of Agreement (IA) are applied to the experiments in this paper. Compared with other machine learning methods, the experimental results showed that the forecasting accuracy of the proposed CNN-LSTM model (APNet) is verified to be the highest in this paper. For the CNN-LSTM model, its feasibility and practicability to forecast the PM2.5 concentration are also verified in this paper. The main contribution of this paper is to develop a deep neural network model that integrates the CNN and LSTM architectures, and through historical data such as cumulated hours of rain, cumulated wind speed and PM2.5 concentration. In the future, this study can also be applied to the prevention and control of PM2.5.

A Deep CNN-LSTM Model for Particulate Matter (PM 2.5 ) Forecasting in Smart Cities.

A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory.

Air pollution is well recognized as a major risk factor for chronic non-communicable diseases and has been estimated to contribute more to global morbidity and mortality than all other known environmental risk factors combined. Although air pollution contains a heterogeneous mixture of gases, the most robust evidence for detrimental effects on health is for fine particulate matter (particles ≤2.5 µm in diameter (PM2.5)) and ozone gas and, therefore, these species have been the main focus of environmental health research and regulatory standards. The evidence to date supports a strong link between the risk of cardiovascular events and all-cause mortality with PM2.5 across a range of exposure levels, including to levels below current regulatory standards, with no ‘safe’ lower exposure levels at the population level. In this comprehensive Review, the empirical evidence supporting the effects of air pollution on cardiovascular health are examined, potential mechanisms that lead to increased cardiovascular risk are described, and measures to reduce this risk and identify key gaps in our knowledge that could help address the increasing cardiovascular morbidity and mortality associated with air pollution are discussed. A strong relationship exists between exposure to air pollution and cardiovascular events. In this Review, Rajagopalan and colleagues summarize the evidence supporting the detrimental effects of air pollution on cardiovascular health and describe the potential mechanisms involved in air pollution-mediated cardiovascular mortality.

Environmental determinants of cardiovascular disease: lessons learned from air pollution.

Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions

Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation

Ground level monitoring of Particulate Matter (PM) is limited by spatial coverage and resolution, in spite of possessing high temporal resolution and accuracy. Atmospheric Aerosol Optical Depth (AOD), a product of space-borne remote sensing, has shown significant potential for estimating ground level PM concentrations. Several approaches have been used to improve the correlation between AOD-PM by providing corrections for the aerosol vertical profile and ground level humidity. However, the effects of the vertical profile of humidity and aerosol size on the AOD-PM relationship requires further study. In this paper, we propose a method for developing an AOD-PM_(2.5) relationship by retrieving the vertical profile of relative humidity via ground observation data and aerosol size distribution in Beijing. Moreover, a series of Hanel growth coefficients (γ) are applied to determine the specific value, which maximizes the correlation. The results show that applying our proposed method can improve the correlation from R = 0.610 to R = 0.707 for Terra and R = 0.707 to 0.752 for Aqua. The best correlations were obtained for γ = 1.2 and 1.3 for Terra and Aqua, respectively. A good correlation (R = 0.8) between ground based and MODIS based PM_(2.5) measurements, together with employing MODIS to predict true air pollution levels (65% accuracy), suggests that the vertical profile of RH derived via ground level observation and aerosol size should be considered and applied to models in future studies, which utilize satellite data for air pollution monitoring and controlling.

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Lingyan Jin

Papers

Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation

An Improved Method for Monitoring Fine Particulate Matter Mass Concentrations via Satellite Remote Sensing