Forecasting Of PM2 Research Articles

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

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.

The Forecasting of PM2.5 Using a Hybrid Model Based on Wavelet Transform and an Improved Deep Learning Algorithm

In recent years, the haze has caused serious troubles to people's lives, with the continuous increase of PM2.5 emissions. The accurate prediction of PM2.5 is very crucial for policy makers to make predictive measures. Due to the nonlinearity of the PM2.5 time series, it is difficult to predict accurately. Despite some studies about PM2.5 being proposed, the problem of the LSTM (long short-term memory) gradient disappearance and random selection of wavelet orders and layers isn't still solved. In this study, a novel model based on WT (wavelet transform)-SAE (stacked autoencoder)-LSTM is proposed. Firstly, six study sites from China are taken as examples and WT is used to decompose PM2.5 time series into several low-and high- frequency components based on different samples. Secondly, the decomposed components are predicted based on SAE-LSTM. Finally, the predicted results are reconstructed in view of all low-and high-frequency components and the predicted results are obtained. The results imply that: (1) the forecasting performance of SAE-LSTM is better than that of other models (e.g., BP (back propagation)) used for comparison; (2) for six different PM 2.5 samples, four orders five layers, five orders six layers, five orders seven layers, three orders six layers, five orders seven layers, and five orders six layers are the most appropriate. The conclusion that such a novel model may help to enhance the accuracy of PM 2.5 prediction can be drawn.

Multi-output support vector machine for regional multi-step-ahead PM2.5 forecasting

Air quality deteriorates fast under urbanization in recent decades. Reliable and precise regional multi-step-ahead PM2.5 forecasts are crucial and beneficial for mitigating health risks. This work explores a novel framework (MM-SVM) that combines the Multi-output Support Vector Machine (M-SVM) and the Multi-Task Learning (MTL) algorithm for effectively increasing the accuracy of regional multi-step-ahead forecasts through tackling error accumulation and propagation that is commonly encountered in regional forecasting. The Single-output SVM (S-SVM) is implemented as a benchmark. Taipei City of Taiwan is our study area, where three types of air quality monitoring stations are selected to represent areas imposed with high traffic influences, high human activities and commercial trading influences, and less human interventions close to nature situation, respectively. We consider forecasts of PM2.5 concentrations as a function of meteorological and air quality factors based on long-term (2010–2016) observational datasets. Firstly, the Kendall tau coefficient is conducted to extract key spatiotemporal factors from regional meteorological and air quality inputs. Secondly, the M-SVM model is trained by the MTL to capture non-linear relationships and share correlation information across related tasks. Lastly, the MM-SVM model is validated using hourly time series of PM2.5 concentrations as well as meteorological and air quality datasets. Regarding the applicability of regional multi-step-ahead forecasts, the results demonstrate that the MM-SVM model is much more promising than the S-SVM model because only one forecast model (MM-SVM) is required, instead of constructing a site-specific S-SVM model for each station. Moreover, the forecasts of the MM-SVM are found better consistent with observations than those of any single S-SVM in both training and testing stages. Consequently, the results clearly demonstrate that the MM-SVM model could be recommended as a novel integrative technique for improving the spatiotemporal stability and accuracy of regional multi-step-ahead PM2.5 forecasts.

Trend analysis and forecast of PM2.5 in Fuzhou, China using the ARIMA model

Atmospheric haze from particulate matter has become a major health concern in many large and medium-sized cities of China. Fine airborne particles, PM2.5, are a major component of haze, and the reasons for their fluctuating concentrations need to be better understood to improve air quality. While much of the air quality data from the China National Environmental Monitoring Centre (CNEMC) has already been analyzed for larger Chinese cities such as Beijing and Shanghai, the data from Fuzhou is the topic of this study. Time series data of pollutant concentrations and meteorological parameters were accessed for Fuzhou from August 2014 to July 2016, covering two cold periods (November through February) and two warm periods (May through July). PM2.5 concentrations were compared with those of other pollutants and with meteorological parameters, and the AutoRegressive Integrated Moving Average (ARIMA) model was applied to forecast PM2.5 concentrations. The correlation analysis of Spearman’s rho showed that PM2.5 concentrations had significant positive correlations with PM10, SO2 and NO2 concentrations, while they had negative correlations with meteorological parameters. The ARIMA results showed that PM2.5 concentrations experienced seasonal fluctuations over the two years, which were higher in the cold periods and lower in the corresponding warm periods, ranging from 23 to 52 μg/m3 and from 19 to 31 μg/m3, respectively. Average PM2.5 concentrations during these same cold periods and warm periods were 35 μg/m3 over the two cold periods, which was 52% higher than the 23 μg/m3 averaged over the two warm periods. Compared to the two observed years, 2014–15 and 2015–16, the forecast of PM2.5 concentrations for 2016–17, ranging from about 15 to 30 μg/m3, exhibited similar seasonal fluctuations but also reduced in the same period, ranging from 19 to 52 μg/m3. It is speculated that the Fuzhou Government's macro-policy on air quality may be one of the reasons for the decrease in PM2.5 concentration during the cold periods. Possible causes were given for the discrepancies between actual and fit values.

Hybrid forecasting of PM2.5 using SOFM and ELM

The article presents a new approach to atmospheric PM2.5 dust prediction using an Extreme Learning Machine (ELM) neural network with clusterization done by the Self Organizing Feature Map (SOFM). This work is concerned with the calculation of the average level of air particulate matters PM2,5 in Warsaw's Ursynow one day ahead. The brief description of the hazards posed by air pollution is included. The work presents a short description of the SOFM and ELM networks, and their hybridized system used as a prediction tool. The analysis of the obtained results was presented and discussed.

Spatial Distribution Characteristics and Hourly Forecast of PM2.5 Pollution in Summer in Beijing

The fine particulate matter (PM2.5) has become the primary pollutant of atmospheric environment of Beijing now, therefore to know the spatial distribution characteristics and to make accurate and refined forecast of PM2.5 is very important. In this study, the spatial distribution characteristics of PM2.5 concentration in summer in Beijing was derived by using Kriging interpolation method based on the data from 30 air quality monitoring stations all over Beijing during June, July and August 2014. In general, the PM2.5 pollution is higher in the south and east, and is lower in the north and west, but when there is south or southeast wind blowing, the situation could be totally the opposite. Based on the hourly PM2.5 concentration data from one representative air quality monitoring station and the meteorological data from a nearby meteorological station during June, July and August 2014, the hourly PM2.5 concentration was forecasted up to 168 hours ahead by using BP (Error-back propagation) and RBF (Radial basis function) neural network methods. The results show RBF neural network method is more efficient, the curve trends of the forecasted values are similar with the curve trends of the monitored values and the forecasted values have significant linear relationship with the monitored values, which demonstrates the possibility of hourly forecast for PM2.5 pollution.

Evaluation of several PM2.5 forecast models using data collected during the ICARTT/NEAQS 2004 field study

Real‐time forecasts of PM2.5 aerosol mass from seven air quality forecast models (AQFMs) are statistically evaluated against observations collected in the northeastern United States and southeastern Canada from two surface networks and aircraft data during the summer of 2004 International Consortium for Atmospheric Research on Transport and Transformation (ICARTT)/New England Air Quality Study (NEAQS) field campaign. The AIRNOW surface network is used to evaluate PM2.5 aerosol mass, the U.S. EPA STN network is used for PM2.5 aerosol composition comparisons, and aerosol size distribution and composition measured from the NOAA P‐3 aircraft are also compared. Statistics based on midday 8‐hour averages, as well as 24‐hour averages are evaluated against the AIRNOW surface network. When the 8‐hour average PM2.5 statistics are compared against equivalent ozone statistics for each model, the analysis shows that PM2.5 forecasts possess nearly equivalent correlation, less bias, and better skill relative to the corresponding ozone forecasts. An analysis of the diurnal variability shows that most models do not reproduce the observed diurnal cycle at urban and suburban monitor locations, particularly during the nighttime to early morning transition. While observations show median rural PM2.5 levels similar to urban and suburban values, the models display noticeably smaller rural/urban PM2.5 ratios. The ensemble PM2.5 forecast, created by combining six separate forecasts with equal weighting, is also evaluated and shown to yield the best possible forecast in terms of the statistical measures considered. The comparisons of PM2.5 composition with NOAA P‐3 aircraft data reveals two important features: (1) The organic component of PM2.5 is significantly underpredicted by all the AQFMs and (2) those models that include aqueous phase oxidation of SO2 to sulfate in clouds overpredict sulfate levels while those AQFMs that do not include this transformation mechanism underpredict sulfate. Errors in PM2.5 ammonium levels tend to correlate directly with errors in sulfate. Comparisons of PM2.5 composition with the U.S. EPA STN network for three of the AQFMs show that sulfate biases are consistently lower at the surface than aloft. Recommendations for further research and analysis to help improve PM2.5 forecasts are also provided.