Research on PM_2.5 Concentration Prediction Algorithm Based on Temporal and Spatial Features

PM_2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate, and it is an evaluation indicator of air pollution level. Achieving PM_2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control. The past methods neglected the impact caused by PM_2.5 flow between cities when analyzing the impact of inter-city PM_2.5 concentrations, making it difficult to further improve the prediction accuracy. However, factors including geographical information such as altitude and distance and meteorological information such as wind speed and wind direction affect the flow of PM_2.5 between cities, leading to the change of PM_2.5 concentration in cities. So a PM_2.5 directed flow graph is constructed in this paper. Geographic and meteorological data is introduced into the graph structure to simulate the spatial PM_2.5 flow transmission relationship between cities. The introduction of meteorological factors like wind direction depicts the unequal flow relationship of PM_2.5 between cities. Based on this, a PM_2.5 concentration prediction method integrating spatial-temporal factors is proposed in this paper. A spatial feature extraction method based on weight aggregation graph attention network (WGAT) is proposed to extract the spatial correlation features of PM_2.5 in the flow graph, and a multi-step PM_2.5 prediction method based on attention gate control loop unit (AGRU) is proposed. The PM_2.5 concentration prediction model WGAT-AGRU with fused spatiotemporal features is constructed by combining the two methods to achieve multi-step PM_2.5 concentration prediction. Finally, accuracy and validity experiments are conducted on the KnowAir dataset, and the results show that the WGAT-AGRU model proposed in the paper has good performance in terms of prediction accuracy and validates the effectiveness of the model.     

稀疏组lasso罚向量自回归模型的大气污染物预测:京津冀案例研究

进行了大气污染物预测研究。针对传统的向量自回归模型方法所面临的过参数化问题,提出了稀疏组lasso罚向量自回归模型并应用近邻梯度下降法求解模型参数。为了验证模型的有效性,将其应用于2015年京津冀大气污染物数据中并对2016年1月1日北京6项大气污染物浓度进行预测。实验数据表明:基于稀疏组lasso罚模型的PM2.5预测归一化均方误差约为3.8%,预测精度高于向量自回归(VAR)模型、基于各种稀疏结构的向量自回归(VAR-L)模型、分层向量自回归(HVAR)模型。此外,京津冀不同城市对北京的空气质量影响程度不同,这可以通过组内稀疏模型参数进行解释。将凸优化概念与向量自回归模型结合应用于大气污染物浓度的预测中,对京津冀大气污染协同治理具有重要意义。     

COVID19: Forecasting Air Quality Index and Particulate Matter (PM2.5)

Urbanization affects the quality of the air, which has drastically degraded in the past decades. Air quality level is determined by measures of several air pollutant concentrations. To create awareness among people, an automation system that forecasts the quality is needed. The COVID-19 pandemic and the restrictions it has imposed on anthropogenic activities have resulted in a drop in air pollution in various cities in India. The overall air quality index (AQI) at any particular time is given as the maximum band for any pollutant. PM2.5 is a fine particulate matter of a size less than 2.5 micrometers, the inhalation of which causes adverse effects in people suffering from acute respiratory syndrome and other cardiovascular diseases. PM2.5 is a crucial factor in deciding the overall AQI. The proposed forecasting model is designed to predict the annual PM2.5 and AQI. The forecasting models are designed using Seasonal Autoregressive Integrated Moving Average and Facebook’s Prophet Library through optimal hyperparameters for better prediction. An AQI category classification model is also presented using classical machine learning techniques. The experimental results confirm the substantial improvement in air quality and greater reduction in PM2.5 due to the lockdown imposed during the COVID-19 crisis.     

辽宁中部城市群可吸入颗粒物PM_(10)、PM_(2.5)的污染水平

利用辽宁中部沈阳、鞍山、抚顺和本溪4个城市2006年8月—2007年10月可吸入颗粒物PM10、PM2.5、PM1的监测资料及同步气象因子的监测资料,分析了其分布特征、污染水平及其与气象因子的关系。结果表明:受区域天气系统的影响,4个城市PM10、PM2.5的日均浓度变化趋势基本一致,具有区域分布特征;PM10超标率冬季最高,PM2.5超标率冬季最高,夏季7月份也较高;PM2.5日均浓度占PM10日均浓度的比例夏季或冬季最大,春季4、5月份最小;PM10、PM2.5和PM1之间有很好的相关性;PM10与风速、温度呈负相关,PM2.5和PM1与能见度、风速、温度呈负相关,与相对湿度成正相关。     

A New Model Using Multiple Feature Clustering and Neural Networks for Forecasting Hourly PM2.5 Concentrations,and Its Applications in China

Particulate matter with an aerodynamic diameter no greater than 2.5 μm (PM_2.5) concentration forecasting is desirable for air pollution early warning. This study proposes an improved hybrid model, named multi-feature clustering decomposition (MCD)–echo state network (ESN)–particle swarm optimization (PSO), for multi-step PM_2.5 concentration forecasting. The proposed model includes decomposition and optimized forecasting components. In the decomposition component, an MCD method consisting of rough sets attribute reduction (RSAR), k-means clustering (KC), and the empirical wavelet transform (EWT) is proposed for feature selection and data classification. Within the MCD, the RSAR algorithm is adopted to select significant air pollutant variables, which are then clustered by the KC algorithm. The clustered results of the PM_2.5 concentration series are decomposed into several sublayers by the EWT algorithm. In the optimized forecasting component, an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation. The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor. Real PM_2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model. The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM_2.5 concentrations and has better performance than the benchmark models.     

2009年北京市春季大气颗粒PM_(2.5)和黑碳浓度变化特征

为了评价奥运会后车辆限行、施工减少等措施对北京市大气环境质量的影响,利用黑碳仪和颗粒物在线观测仪,于2009年4月26日—5月16日对北京市大气悬浮颗粒PM2.5质量浓度,2009年4月21日—5月21日对黑碳浓度实行连续观测,采用SPSS11.5和EXCEl2003对数据进行统计分析,获PM2.5和黑碳的日均值、小时均值和观测时段内小时均值的连续变化资料。结果表明:观测时段内PM2.5浓度日均值为(9.3±0.2)μg/m3,低于北京市以往同期记录,达到美国EPA的PM2.5推荐标准。黑碳浓度的日均值为(2319±18)ng/m3,低于我国其他城市和北京市历史记录。说明北京市实行的污染源控制手段收到了明显效果。PM2.5浓度呈现周变化趋势,日变化表现两个峰值。黑碳浓度日变化为一峰一谷,未出现以往研究的两个峰值,推测可能受晚间车辆和烹饪活动的影响,晚间峰值被次日升高趋势遮盖。     

Gaussian Support Vector Machine Algorithm Based Air Pollution Prediction

Air pollution is one of the major concerns considering detriments to human health. This type of pollution leads to several health problems for humans, such as asthma, heart issues, skin diseases, bronchitis, lung cancer, and throat and eye infections. Air pollution also poses serious issues to the planet. Pollution from the vehicle industry is the cause of greenhouse effect and CO2 emissions. Thus, real-time monitoring of air pollution in these areas will help local authorities to analyze the current situation of the city and take necessary actions. The monitoring process has become efficient and dynamic with the advancement of the Internet of things and wireless sensor networks. Localization is the main issue in WSNs; if the sensor node location is unknown, then coverage and power and routing are not optimal. This study concentrates on localization-based air pollution prediction systems for real-time monitoring of smart cities. These systems comprise two phases considering the prediction as heavy or light traffic area using the Gaussian support vector machine algorithm based on the air pollutants, such as PM2.5 particulate matter, PM10, nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), and sulfur dioxide (SO2). The sensor nodes are localized on the basis of the predicted area using the meta-heuristic algorithms called fast correlation-based elephant herding optimization. The dataset is divided into training and testing parts based on 10 cross-validations. The evaluation on predicting the air pollutant for localization is performed with the training dataset. Mean error prediction in localizing nodes is 9.83 which is lesser than existing solutions and accuracy is 95%.     

Research on the prediction of LOCA condition in nuclear power plants based on GRU recurrent neural network and its variants

Considering the development of deep learning and the emergence of intelligent control demands in nuclear reactors, along with the presence of plant-level real-time information monitoring systems in most nuclear power plants, there is a considerable accumulation of sensor measurements from long-term operation. This makes it feasible to conduct medium to long-term predictions for various real-time conditions in nuclear power plants. Therefore, this paper proposes the utilization of a gate-based recurrent neural network called GRU (Gated Recurrent Unit) and its variants for parameter prediction of LOCA (Loss of Coolant Accident) scenarios. The main content of this paper consists of two parts: (1) Experimental verification is conducted to demonstrate that GRU has excellent capability in capturing long-term sequential information and generalization ability, making it suitable for predicting accident conditions in nuclear power plants. Two accident trend prediction methods based on the GRU network are proposed for scenarios with limited data. The results show that these methods can effectively provide short-term development trends for accident conditions. Additionally, by considering the feature extraction capacity of CNN, the fusion of CNN and GRU models is employed for parameter prediction under different sizes of broken area. The results indicate an improvement in the model's generalization ability. (2) In scenarios with limited and incomplete data, a more robust variant of GRU called GRU-D model is utilized for both univariate and multivariate synchronous prediction of accident conditions with different missing values. Experimental results demonstrate that even with a data missing rate of 90%, the GRU-D network exhibits excellent predictive accuracy and generalization ability in parameter prediction for the given conditions.     

Measuring and modeling particulate dispersion: a case study of Kerman Cement Plant

In this study to identify the origin of PM10 in the atmosphere of Kerman and investigate the dispersion conditions for these particles, the variations of the mass concentration and size distribution of PM10 have been measured. This study is focused on the local environmental impact of Kerman Cement Plant. All samples have been taken in the area between the plant and the city entrance at the wind direction. The result of this research shows that the PM10 concentration in the ambient air in distances about 590-1370 m from the stacks is higher than the WHO guidelines of annual average (260 microg/m(3)). Also, concentration of PM10 is computed by using Gaussian plume model that incorporates source related factors and meteorological factors to estimate pollutant concentration from continuous sources. The performance of this model has been compared with the measured data.     

A Haze Feature Extraction and Pollution Level Identification Pre-Warning Algorithm

The prediction of particles less than 2.5 micrometers in diameter (PM2.5) in fog and haze has been paid more and more attention, but the prediction accuracy of the results is not ideal. Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze. In order to improve the effects of prediction, this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning. Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze, and deep confidence network is utilized to extract high-level features. eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features, as well as predict haze. Establish PM2.5 concentration pollution grade classification index, and grade the forecast data. The expert experience knowledge is utilized to assist the optimization of the pre-warning results. The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine (SVM) and Back Propagation (BP) widely used at present, the accuracy has greatly improved compared with SVM and BP.     

室内PM2.5浓度标准的探讨

PM 2.5在室内颗粒物中占有很大比例,由于具有比表面积大的特点,对多种有机物具有较强的吸附能力,可以直接进入肺泡,导致年总死亡率、心肺疾病死亡率以及肺癌死亡率的增加,对人体产生全方位的影响。所以将PM 2.5纳入我国室内空气质量检测范围和评价体系是加强空气污染防治、保障人体健康的必然要求。通过分析一些国家和国际组织的PM 2.5标准、我国《环境空气质量标准》及室内PM2.5的检测标准,对我国《室内空气质量标准》中PM2.5的浓度标准值进行探讨,为室内PM 2.5浓度的控制提出明确的目标与方向。     

2011年春季上海市一次典型污染过程及气溶胶的垂直分布特征

利用多种地面观测资料,研究2011年4月14—15日上海的一次典型污染过程,分析生消机制、污染物来源以及气溶胶垂直分布的演变过程。结果表明,14、15日PM2.5的日平均质量浓度分别为78.9、115.9μg/m3,均超过环境空气质量标准中PM2.5质量浓度的二级标准;污染过程形成于稳定天气形势下污染物的积累,结束于短时降水和冷空气南下的共同作用;污染物主要源于上海本地及其西南方的局地污染;污染天气对流层低层的消光系数远大于非污染天气的。     

Application of AERMOD for short-term air quality prediction with forecasted meteorology using WRF model

Many methods are available for air quality forecasting based on statistical and back trajectory models which require past time series data. Future air quality prediction through models is the best tool to make rational decisions by policy maker. Limited work has been done on air quality forecasting using dispersion models which require better meteorological boundary conditions. The Weather Research and Forecasting (WRF) and American Meteorological Society/Environmental Policy Agency Regulatory Model (AERMOD) models have not yet been combined for air quality forecasting. Here, a case study has been carried out to forecast air quality using onsite meteorological data from WRF model and a dispersion model named AERMOD. Prior to the use of AERMOD, a comprehensive emission inventory has been prepared for all the sources in the study region Chembur of Mumbai city. Chembur has been notified as the “air pollution control region” by local authority due to high levels of air pollution caused by the presence of four major industries, six major roads in addition to a crematorium and a biomedical waste incineration facility. The WRF–AERMOD system was applied for prediction of concentration levels of pollutants SO₂, NO _x and PM₁₀. A reasonable agreement was obtained when predicted values were compared with observed data. Results of the study indicated that forecasting of air quality can be carried out using AERMOD with forecasted meteorological parameters derived from WRF without any requirement of past time series air quality data. Such kind of forecasting method can be used for air quality management of any region by policy makers.     

Air Quality Prediction Based on Kohonen Clustering and ReliefF Feature Selection

Air quality prediction is an important part of environmental governance. The accuracy of the air quality prediction also affects the planning of people’s outdoor activities. How to mine effective information from historical data of air pollution and reduce unimportant factors to predict the law of pollution change is of great significance for pollution prevention, pollution control and pollution early warning. In this paper, we take into account that there are different trends in air pollutants and that different climatic factors have different effects on air pollutants. Firstly, the data of air pollutants in different cities are collected by a sliding window technology, and the data of different cities in the sliding window are clustered by Kohonen method to find the same tends in air pollutants. On this basis, combined with the weather data, we use the ReliefF method to extract the characteristics of climate factors that helpful for prediction. Finally, different types of air pollutants and corresponding extracted the characteristics of climate factors are used to train different sub models. The experimental results of different algorithms with different air pollutants show that this method not only improves the accuracy of air quality prediction, but also improves the operation efficiency.     

Ovarian Dysfunction Induced by Chronic Whole‐Body PM2.5 Exposure

Fine particulate matter (PM2.5) pollution arouses public health concerns over the world. Increasing epidemiologic evidence suggests that exposure to ambient airborne PM2.5 increases the risk of female infertility. However, relatively few studies have systematically explored the harmful effect of chronic PM2.5 exposure on ovarian function and the underlying mechanisms. In this study, female C57BL/6J mice are exposed to filtered air or urban airborne PM2.5 for 4 months through a whole‐body exposure system. It is found that PM2.5 exposure significantly caused the alteration of estrus cycles, reproductivity, hormone levels, and ovarian reserve. The granulosa cell apoptosis via the mitochondria dependent pathway contributes to the follicle atresia. With RNA‐sequencing technique, the differentially expressed genes induced by PM2.5 exposure are mainly enriched in ovarian steroidogenesis, reactive oxygen species and oxidative phosphorylation pathways. Furthermore, it is found that increased PM2.5 profoundly exacerbated ovarian oxidative stress and inflammation in mice through the NF‐κB/IL‐6 signaling pathway. Notably, dietary polydatin (PD) supplement has protective effect in mice against PM2.5‐induced ovarian dysfunction.These striking findings demonstrate that PM2.5 and/or air pollution is a critical factor for ovarian dysfunction through mitochondria‐dependent and NF‐κB/IL‐6‐mediated pathway, and PD may serve as a pharmaceutic candidate for air pollution‐associated ovarian dysfunction.     

杭州市下沙地区PM2.5浓度监测与分析

利用便携式的空气质量检测仪,对杭州市下沙地区五条道路及三个小区进行了PM2.5的测量.2010年下沙道路PM2.5的质量浓度在40～136μg/m3,2012年道路PM2.5为18～67μg/m3,说明下沙地区空气质量近年得到改善.同时还对不同空间分布的道路与小区空气质量进行了对比,五条道路按空气质量由好到坏依次为:沿江边的之江东路、高教园学源街、靠近商业区文渊路、途经高架桥的学林街以及靠近工业区的围垦街.三个小区空气质量由好到坏依次为:居住密集区内的小区、高教园周边小区、商业区周边小区.分析了影响PM2.5的因素,认为道路上汽车排放的尾气是小区的污染源之一,而天气因素对PM2.5的多寡有很大影响.     

Spatio-temporal analysis with short- and long-memory dependence: a state-space approach

This paper deals with the estimation and prediction problems of spatio-temporal processes by using state-space methodology. The spatio-temporal process is represented through an infinite moving average decomposition. This expansion is well known in time series analysis and can be extended straightforwardly in space–time. Such an approach allows easy implementation of the Kalman filter procedure for estimation and prediction of linear time processes exhibiting both short- and long-range dependence and a spatial dependence structure given on the locations. Furthermore, we consider a truncated state-space equation, which allows to calculate an approximate likelihood for large data sets. The performance of the proposed Kalman filter approach is evaluated by means of several Monte Carlo experiments implemented under different scenarios, and it is illustrated with two applications.     

Source apportionment of PM2.5 in Beijing in 2004

Based on measurements of fine particulate matter (PM2.5, i.e., particles with an aerodynamic diameter of 2.5 microm or less) in January and August 2004, serious air pollution persists in Beijing. The chemical analysis included organic and elemental carbon, water-soluble ions, and elemental compositions. The positive matrix factorization (PMF) method was used to apportion the PM2.5 sources. The sources contributing dominantly to PM2.5 mass concentrations are coal combustion in winter and the secondary products in summer. Furthermore, the contributions from motor vehicles, road dusts and biomass burning could not be neglected. The products of biomass burning for winter heating in the area around Beijing could enter the urban area during quasi-quiescent weather conditions. In conclusion, some effective control measures were proposed to reduce the PM2.5 pollution in Beijing.     

一种改进的雁群扩展粒子群算法研究

针对粒子群算法易陷入局部最优的问题,结合雁群启示粒子群算法和扩展粒子群算法提出了基于雁群启示的扩展粒子群(GeEPSO)算法。该算法在利用雁群飞行方向的多样性同时融合了所有粒子的个体极值信息,提高了种群多样性。为进一步提高改进算法的收敛速度,引入简化粒子群提出了 GeESPSO算法。基准函数的仿真表明:改进算法GeESPSO较好地平衡了收敛速度和局部最优两个矛盾,总体较优。为进一步验证算法在实际应用中的有效性,又分别用两种改进算法优化BP神经网络,并用相关气象数据对PM2.5的值进行预测。     

基于GRU神经网络的自相关过程残差控制图

为提升自相关过程监控的效率,提出基于门控循环单元(gated recurrent unit,GRU)神经网络的自相关过程残差控制图。采用受控下的自相关过程数据对GRU网络进行离线训练与测试,对预测误差进行监控,形成控制用残差控制图。采用训练好的GRU网络预测当前过程波动,利用控制用残差控制图判定当前过程是否失控。运用蒙特卡洛仿真法,与基于一阶自回归模型、BP神经网络以及支持向量回归构建的残差控制图进行性能对比。研究表明,过程受控时,所提残差控制图与其他3种的稳态平均运行链长相差不大,即4者的性能表现相当;而在均值偏移异常过程中,所提残差控制图的平均运行链长远小于其他3种,对自相关过程均值偏移具有较好的监控性能。