Query: Temporal characteristics and forecasting of PM2.5 concentration based on historical data in Houston, USA

title: Temporal characteristics and forecasting of PM2.5 concentration based on historical data in Houston, USA
abstract: Urban PM2.5 has been recognized as a critical type of air pollutions that could damage the human respiratory system. Numerous studies have aimed to perform spatiotemporal assessment on diverse urban areas and proposed various PM2.5 forecasting models. Geographical variations and the inadequacy of hourly PM2.5 concentration forecasting are the main limitations of previous researches. The objectives of this paper are to elaborate the temporal characteristics of PM2.5 concentration of each geographic region in Houston, Texas, USA, from inland rural to coastal areas, and to build a forecasting model that could perform daily and hourly PM2.5 predictions. This research analyzed historical PM2.5 concentration data from 2008 to 2017 collected from five regions in Houston by the Texas Commission on Environmental Quality (TCEQ) Temporal characteristics calculation equations were provided. Analysis results revealed several important temporal characteristics of PM2.5 concentrations in Houston. The annual average daily PM2.5 concentration showed decreasing trends from 2008 to 2017. Higher PM2.5 concentration levels were observed in summer months than winter months. In downtown area the PM2.5 concentration levels were higher on weekdays than weekends. In rural, suburban, residential, and coastal areas the PM2.5 concentration levels were lower on weekdays than weekends. Moreover, PM2.5 concentration levels were significantly higher during traffic rush hours than other time intervals. The accuracy and precision of forecasting have been improved based on a combination of factors compared to the forecasting based solely on day of week factors (DWF) hourly concentration factors (HCF) and monthly concentration factors (MCF) Advantages of such combined forecasting include lowering calculation complexity and providing hourly concentration forecasting. Look up tables for each temporal characteristic factor were provided that could be easily applied for further forecasting.

title: A Prediction of PM2.5 Concentration Based on Temporal Spatial Fusion Model
abstract: In this paper, a temporal spatial fusion model is proposed for PM2.5 concentration prediction. The model uses historical PM2.5 concentration and meteorological data as input of the model to make hourly predictions of PM2.5 concentration. This model consists of three parts: 1) Long short term memory neural network predictor based on time dimension, 2) Artificial neural network predictor based on spatial dimension, 3) Model tree predictor based on temporal spatial fusion. This method combines the forecast results of two dimensions in space and time dynamically, as the spatial and temporal correlation of data is considered. Experimental results show this model performs better than predicting from a single dimension, confirming the effectiveness of the model.

title: A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short term memory.
abstract: Increasing availability of data related to air quality from ground monitoring stations has provided the chance for data mining researchers to propose sophisticated models for predicting the concentrations of different air pollutants. In this paper, we proposed a hybrid model based on deep learning methods that integrates Graph Convolutional networks and Long Short Term Memory networks (GC LSTM) to model and forecast the spatiotemporal variation of PM2.5 concentrations. Specifically, historical observations on different stations are constructed as spatiotemporal graph series, and historical air quality variables, meteorological factors, spatial terms and temporal attributes are defined as graph signals. To evaluate the performance of the GC LSTM, we compared our results with several state of the art methods in different time intervals. Based on the results, our GC LSTM model achieved the best performance for predictions. Moreover, evaluations of recall rate (68.45% false alarm rate (4.65% (both of threshold: 115 mg/m3) and correlation coefficient R2 (0.72) for 72 hour predictions also verify the feasibility of our proposed model. This methodology can be used for concentration forecasting of different air pollutants in future.

title: Long short term memory Fully connected (LSTM FC) neural network for PM2.5 concentration prediction.
abstract: People have been suffering from air pollution for a decade in China, especially from PM2.5 (particulate matter with a diameter of less than 2.5 mm) Accurate prediction of air quality has great practical significance. In this paper, we propose a data driven model, called as long short term memory fully connected (LSTM FC) neural network, to predict PM2.5 contamination of a specific air quality monitoring station over 48 h using historical air quality data, meteorological data, weather forecast data, and the day of the week. Our predictive model consists of two components: (1) Using a long short term memory (LSTM) based temporal simulator to model the local variation of PM2.5 contamination and (2) Using a neural network based spatial combinatory to capture spatial dependencies between the PM2.5 contamination of central station and that of neighbor stations. We evaluate our model on a dataset containing records of 36 air quality monitoring stations in Beijing from 2014/05/01 to 2015/04/30 and compare it with artificial neural network (ANN) and long short term memory (LSTM) models on the same dataset. The results show that our LSTM FC neural network model gives a better predictive performance.

title: A Hybrid Time Series Model based on Dilated Conv1D and LSTM with Applications to PM2.5 Forecasting
abstract: PM2.5 forecasting is crucial because it affects people's physical health, but also provides guidance for pollution control. Since the prediction of PM2.5 involves various meteorological factors as well as the influence of historical data, in this paper, we take the prediction of PM2.5 as a multivariable time series prediction problem, and propose a hybrid time series model based on LSTM and dilated convolution networksDConvLSTM. The model can capture the internal long term dependence and external impact factors from a large amount of historical data, and make a prediction of PM2.5 concentration in the future period. DConvLSTM can identify the simple time pattern from the sequence data through the Dilated CNN in one dimensional, where the added cavity is used to filter the sequence signal and increase the receptive field. LSTM is a variant of RNN, which naturally has the characteristics of learning long term dependence from time series data. The DConvLSTM is composed of a one dimensional Dilated CNN, LSTM and a completely connected layer. In this paper, DConvLSTM model is applied to the prediction of PM2.5 concentration in Beijing. The experiment shows that the proposed model could well predict the PM2.5 concentration in the future period. Comparison with other models, DConvLSTM has a higher fit indicators and computational efficiency.

title: PM2.5 Prediction with a Novel Multi Step Ahead Forecasting Model Based on Dynamic Wind Field Distance
abstract: Particulate matter with a diameter of less than 2.5 mm (PM2.5) has damaged public health globally for a decade. Accurate forecasts of PM2.5 concentration can provide early warnings to prevent the public from hazard exposure. However, existing methods have not considered the available spatiotemporal data sufficiently due to their architecture or inadequate input, and most neglected wind impact on spatiotemporal correlation when selecting related sites. To fill this gap, we proposed a long short term memory convolutional neural network based on dynamic wind field distance (LSTM CNN DWFD) to predict PM2.5 concentration of a specific site for the next 24 h. A KNN method based on dynamic wind field distance was developed and applied to select highly related sites considering wind impact. A local stateful LSTM model was employed to capture temporal correlations in historical air quality and meteorological data for each related site. Then, these temporal features were integrated as a spatiotemporal matrix, and input into CNN for extracting spatiotemporal correlation features. Weather forecasts were also integrated into the model to promote accuracy. Hourly PM2.5 data from 36 monitoring sites in Beijing, China collected from 1 May 2014 to 30 April 2015 were used as experimental dataset. Six fold rolling origin method was employed to conduct experiments on each site, and the results of 216 experiments validated the performance of the proposed LSTM CNN DWFD model. The mean R2 values of the next 1 6 h prediction were 0.85, 0.81, 0.76, 0.70, 0.64, and 0.59, respectively, showing a decrease trend, indicating that the prediction accuracy decreases as the prediction time increases. Comparisons of LSTM CNN DWFD results to results from six other methods show that it delivered higher accuracy PM2.5 predictions, with the mean RMSE (MAE) of 1 6, 7 12, and 13 24 h prediction were 43.90 (29.17) 57.89 (42.16) and 63.14 (47.64) respectively. The results also demonstrate that the sites selected based on dynamic wind field distance are more related to the central site than that based on geographical distance, also contributing to prediction accuracy.

title: Prediction of daily PM2.5 concentration in China using partial differential equations
abstract: Accurate reporting and forecasting of PM2.5 concentration are important for improving public health. In this paper, we propose a partial differential equation (PDE) model, specially, a linear diffusive equation, to describe the spatial temporal characteristics of PM2.5 in order to make short term prediction. We analyze the temporal and spatial patterns of a real dataset from China's National Environmental Monitoring and validate the PDE based model in terms of predicting the PM2.5 concentration of the next day by the former days' history data. Our experiment results show that the PDE model is able to characterize and predict the process of PM2.5 transport. For example, for 300 continuous days of 2016, the average prediction accuracy of the PDE model over all city regions is 93% or 83% based on different accuracy definitions. To our knowledge, this is the first attempt to use PDE based model to study PM2.5 prediction in both temporal and spatial dimensions.

title: Forecasting PM2.5 Concentration Using Spatio Temporal Extreme Learning Machine
abstract: In recent years, air quality has become a severe environmental problem in China. Since bad air quality brought significant influences on traffic and people's daily life, how to predict the future air quality precisely and subtly, has been an urgent and important problem. In this paper, a Spatio Temporal Extreme Learning Machine (STELM) method is proposed for air quality prediction. STELM considers temporal and spatial characteristics of air quality data and related meteorological data, constructs a prediction model based on ELM, and realizes air quality prediction with more than 80% precision. A prototype system is implemented and the experiments on practical air quality data in Beijing validate the effectiveness of our method and system.

title: Spatiotemporal trends of PM2.5 concentrations in central China from 2003 to 2018 based on MAIAC derived high resolution data.
abstract: Long term PM2.5 levels with high precision at fine spatiotemporal resolution are essential for quantitatively understanding the health risk of exposure to ambient fine particulate matter (PM2.5) and making effective air pollution control policies. The emerging statistically derived PM2.5 estimations from satellite remote sensing observations of aerosol optical depth (AOD) data are an effective alternative to reconstruct global, long term, high spatiotemporal resolution PM2.5 information. However, studies on PM2.5 estimation and its application to exposure and health related studies are limited in China due to the lack of historical in situ measurements before 2013. In this study, we explored the long term trends of PM2.5 exposure in central China, a hotspot that has recently been experiencing severe particulate pollution, at the local scale. We first developed a spatiotemporal model incorporating periodical characteristics within the data to estimate daily concentrations of historical PM2.5 at a fine scale of 1 km for 2003 2018. The linear effects of predictors including AOD, meteorological and land use parameters and the non linear interaction between AOD and meteorological parameters were considered in the modeling process. The most recently released high resolution satellite aerosol product, Multi Angle Implementation of Atmospheric Correction (MAIAC) was used to help to represent the fine scale particle gradients. Our daily estimates correlated well with in situ observations (cross validation R2 0.59) achieving precision comparable to previous statistical models. Through linking with gridded demographic data, the population weighted PM2.5 average during 2003 to 2018 was found to be high (62.23 mg/m3 for the whole domain) with obvious spatial variations and seasonality. An inverse U pattern was seen in the time series, with two inflection points around 2008 and 2015. Our model provides reliable particulate information with high spatial resolution and long term temporal coverage, which can inform local scale PM2.5 related epidemiological studies and health risk assessments for central China.

title: Improving air quality forecasting with the assimilation of GOCI AOD retrievals during the KORUS AQ period
abstract: The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal and spatial resolution every day, providing unprecedented information on air pollutants over the upstream region of the Korean peninsula for the last decade. In this study, the GOCI Aerosol optical depth (AOD) retrieved at 550 nm wavelength, is assimilated to ameliorate the analysis quality, thereby making systematic improvements on air quality forecasting in South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics. The preprocessed data are then assimilated in the three dimensional variational data assimilation (3DVAR) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF Chem) During the Korea United States Air Quality (KORUS AQ) period (May 2016) the impact of GOCI AOD on the accuracy of air quality forecasting is examined by comparing with other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and fine particulate matter (PM2.5) observations at the surface. Consistent with previous studies, the assimilation of surface PM2.5 concentrations alone systematically underestimates surface PM2.5 and its positive impact lasts mainly for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecasts are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea.