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。     

Air pollution assessment in urban areas and its impact on human health in the city of Quetta, Pakistan

Nowadays, air over major cities throughout the world has become overburdened with gases produced by automobiles. The death rate due to automobile pollution is increasing rapidly in the metropolitan areas. With passage of time, people realized that polluted air has serious effects on their health, climate and economics. Weather and climate have integrated impact on human activities resulting in worldwide concentration of the particulates of environmental pollution, viz., chlorofluorocarbons, carbon dioxide, methane, nitrogen oxide, lead and several other dust and gaseous particles. Like many other mega cities in the world the ambient air quality of Quetta, Pakistan is also deteriorating nowadays. Automobile exhausts and certain industrial pollutants produce O₃ by photochemical reactions. The particulate matter, particularly less than 10 μm in size, can pass through the natural protective mechanism of human respiratory system and plays an important role in genesis and augmentation of allergic disorders. Sources of air pollution in the area and the unique problem arising out of the emission from the vehicles, industries, etc. have been described. Ambient air quality was monitored along with micrometeorological data and the results are discussed. The status of air pollution in the area has been evaluated and a questionnaire survey was conducted to estimate the allergic symptoms and exposure to assess the respiratory disorders. The data are analyzed to evaluate the critical situation arising out of the emission of air pollutants and the impact on human health due to respirable diseases (RDs) in middle class sub-population (activity-wise) in the area assessed. A strategic air quality management plan has been proposed. For the mitigation of air pollution problems in the city, different measures to be adopted to maintain the balance between sustainable development and environmental management have been discussed. Air pollution has significant effects on exacerbation of asthma, allergy and other respiratory diseases.     

中国城市气溶胶危害评价

在环境受到污染的领域,气溶胶对人们生活和人体健康有重要影响。随着城市大气气溶胶污染日趋严重,对中国城市气溶胶危害进行评价十分必要,以便加以控制。本文对如下问题进行了讨论：气溶胶危害评价的领域和问题;空气污染与呼吸道疾病;中国城市空气悬浮颗粒总数（ＴＳＰ）和疾病,评价我国气溶胶对健康的作用;中国城市空气悬浮颗粒总数的主要化学组分,评价其卫生学作用;从卫生保健的角度进行小结和建议。     

Air Pollution Prediction Via Graph Attention Network and Gated Recurrent Unit

PM2.5 concentration prediction is of great significance to environmental protection and human health. Achieving accurate prediction of PM2.5 concentration has become an important research task. However, PM2.5 pollutants can spread in the earth’s atmosphere, causing mutual influence between different cities. To effectively capture the air pollution relationship between cities, this paper proposes a novel spatiotemporal model combining graph attention neural network (GAT) and gated recurrent unit (GRU), named GAT-GRU for PM2.5 concentration prediction. Specifically, GAT is used to learn the spatial dependence of PM2.5 concentration data in different cities, and GRU is to extract the temporal dependence of the long-term data series. The proposed model integrates the learned spatio-temporal dependencies to capture long-term complex spatio-temporal features. Considering that air pollution is related to the meteorological conditions of the city, the knowledge acquired from meteorological data is used in the model to enhance PM2.5 prediction performance. The input of the GAT-GRU model consists of PM2.5 concentration data and meteorological data. In order to verify the effectiveness of the proposed GAT-GRU prediction model, this paper designs experiments on real-world datasets compared with other baselines. Experimental results prove that our model achieves excellent performance in PM2.5 concentration prediction.     

Spatial regression analysis of NOx and O3 concentrations in Madrid urban area using Radial Basis Function networks

This paper discusses the performance of Radial Basis Function networks (RBF) in a problem of spatial regression of pollutants in Madrid. Specifically, the spatial regression of NO_x and O₃ is considered, in such a way that, starting from a set of measuring points provided by the air quality monitoring network of Madrid, the complete surface of the pollutants in the city is obtained. This pollutant surface can be used as an initial step for modeling intra-urban pollution using land-use regression techniques for example. Also, different works has used a pollutant surface to study the patterns of pollution in different cities in the world and also to establish their air monitoring networks under mathematical criteria. The paper is focussed in analyzing the performance of RBF networks to obtain this first pollutant surface, so different RBF training algorithms are tested in this paper. Specifically, evolutionary-based RBF training algorithms are described, and compared with classical training algorithms for RBF networks with Gaussian kernels. The inclusion of meteorological variables in the RBF networks are also discussed in the paper. The experimental part of the article studies real results of the application of RBF networks to obtain a first pollutant surface of NO_x and O₃, using the data of the air pollution monitoring network of Madrid and the meteorological network of the city.     

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%.     

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.     

基于小波变换的网络流量在线预测模型

在无抽取Haar小波变换的基础上,结合自适应AR模型和滑动窗口式多项式拟合方法,建立了一种基于小波变换的递推式高速网络流量在线预测模型.该模型首先用无抽取Haar小波变换把网络流量时间序列分解为细节信号和近似信号,然后对细节信号部分使用自适应AR模型预测,对近似信号部分则使用滑动窗口式多项式拟合方法预测,最后用小波重构获得原始时间序列的预测值.该模型不但提高了流量在线预测的准确性,而且通过模型参数的递推式自动调整,避免了参数的定期估计和更新.     

Performance evaluation of vehicles emissions prediction models

Road traffic is a dominant source of urban air pollution. Therefore, it is necessary to quantify emission levels as accurately as possible to evaluate their impacts on the public health and the environment. Several models were developed to predict these emissions. These models can be grouped into three categories, namely, emission factors models, average speed models, and modal models. The prediction capability of most developed models is relatively poor. Therefore, there is a pressing need to improve the predictability of the existing models or to develop new ones with better accuracy. The main focus of this paper is to review different traffic emissions modeling efforts and to describe the effect of different factors on emission levels and modeling accuracy, so as to get reliable emission estimates. In addition, different models were evaluated for the prediction capability of certain emissions such as carbon monoxide (CO), nitrogen monoxide (NO), nitrogen dioxide (NO₂) and hydrocarbons (HC). These models are mainly based on traffic volume, composition, and flow. The predicted values by one of the models were compared to measured values based on field surveys. The result of comparison indicated that there is a significant difference between the measured and predicted values. These differences ranged from 17% for NO₂ to 72% in the case of CO, which suggests that the NO₂ model has better predictability. This deviation in prediction may be attributed to the fact that prediction models ignored some of the parameters affecting vehicle emissions such as the type of fuel used, air–fuel ratio, engine compression ratio, spark timing, surrounding environment, wind effect, regional characteristics and high pollutants emitters effect.     

Air pollution control in ferroalloy manufacturing industries: an Indian regulatory assessment

Ferroalloy manufacturing involves many unit operations and unit processes. Commencing from the material handling to manufacturing and product collection, a ferroalloy plant emits a wide range of air pollutants. The selection of appropriate air emission control technology is, therefore, very important in such a situation. In this article, few case studies from Indian operating plants are analyzed from the standpoint of installed air pollution control devices. Analyses revealed design flaws in many of these air pollution control devices leading to their collapse with the deterioration of the ambient air quality. As regulatory measures, recommendations are made specifying the air pollution control devices to curb air emission from various stages of the ferroalloy making operations for meeting the air emission standards. Prior to putting forward recommendations in this article, described are the different aspects of the ferroalloy manufacturing with the emphasis of source of emission for both particulate matter as well as gaseous pollutants.     

GA-BP Air Quality Evaluation Method Based on Fuzzy Theory

With the rapid development of China’s economy, the scale of the city has been continuously expanding, industrial enterprises have been increasing, the discharge of multiple pollutants has reached the top of the world, and the environmental problems become more and more serious. The air pollution problem is particularly prominent. Air quality has become a daily concern for people. In order to control air pollution, it is necessary to grasp the air quality situation in an all-round way. It is necessary to evaluate air quality. Accurate results of air quality evaluation can help people know more about air quality. In this paper, refers to previous research results and different evaluation methods, combined with artificial neural network, fuzzy theory, genetic algorithm, GA-BP hybrid algorithm based on fuzzy theory is proposed to evaluate air quality. At the same time, for the problem that the two-grade standard of air quality annual evaluation is not suitable for practical application, the four-grade standard for annual air quality evaluation has been proposed, and its practicality has been verified through experiments. By setting contrast experiments and comparing the air quality evaluation model based on standard BP algorithm, it is proved that the fuzzy GA-BP evaluation model is better than the standard BP model, both in efficiency and accuracy.     

室内污染控制技术研究进展

如何有效地控制室内污染、改善室内空气质量是目前急待解决的问题。本文针对室内污染的来源和特点，指出改善室内空气质量的关键在于消除室内外污染源、合理使用空调通风系统和采取空气净化技术，重点评述了目前主要的室内环境净化处理技术，包括物理吸附法、静电、非平衡等离子、负离子、臭氧、化学试剂、光催化、膜分离和生物净化技术，并分析了各技术的优缺点，同时对室内环境处理技术进行了展望。     

基于实际运行数据的不同气候区冬季空气源热泵结霜特征分析

空气源热泵运行中存在的突出问题是室外机结霜而导致的机组性能下降。在实际应用中,当地气候条件下空气源热泵结霜的难易程度决定了大多数除霜技术的适用性。空气源热泵结霜的难易程度除了受气候影响外,还受系统运行性能甚至用户使用习惯的影响,难以做出定量的判断。因此本文提出结霜度时数的概念,以专门表征气候条件对结霜的影响。通过在国内多地采集空气源热泵实际运行数据,使得结霜度时数的计算更接近实际情况,再对运行数据进行拟合分析得到了不同功率类型的热泵系统的蒸发温度与室外干球温度之间的特征温差,此温差可以用来修正传统设计上所假设的某个定值。最后,根据不同特征温差下的结霜度时数分析,结合不同功率类型的热泵系统在国内275个城市的结霜特征,对这些城市进行了结霜气候分区,为空气源热泵系统进行气候适应性的设计以及控制策略开发提供参考。     

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。     

Performance enhancement of respiratory tumor motion prediction using adaptive support vector regression: Comparison with adaptive neural network method

The breathing motion moves internal organs and targeted regions determined by radiation therapy planning. For the radiation therapy, accurate prediction for breathing motion is of great interest as the outer targeted region treatment could endanger sensitive tissue. In this study, the use of a prediction algorithm with adaptive support vector regression (aSVR) was proposed and compared with the adaptive neural network (ANN) algorithm considering the prediction accuracy and training and predicting time. Respiration data from 87 patients treated by radiation therapy, were acquired with an optical marker at 30 Hz. Five types of prediction filters with the ANN or aSVR filters, were implemented and their performance was compared according to the size of the sliding window (2.5 and 5.0 sec), and the prediction latencies (100, 200, 300, 400, and 500 msec). Training and testing of the prediction algorithms with aSVR and ANN were performed. The root mean square error (RMSE) was used as the accuracy metric. The aSVR with an RBF kernel outperformed other prediction filters, including not only various types of ANN filters but also the aSVR with a linear kernel. A sliding window of 2.5 sec significantly and independently enhanced the overall accuracy. Otherwise, the training and prediction testing times were significantly prolonged in case of aSVR with an RBF kernel. The aSVR filter with the RBF kernel is in all cases superior to other filters regarding its accuracy; it also shows clinically applicable results from the viewpoint of training and predicting time, which may be effective for predicting patient breathing motion and thus enhancing the efficacy of radiation therapy.     

Environmental management plan for port and harbour projects

Port activities can cause deterioration of air and marine water quality in the surrounding areas due to multifarious activities. Hence, for the determination of levels of pollution, identification of pollution sources, control and disposal of waste from various point and non-point sources and for prediction of pollution levels for future, regular monitoring and assessment are required during the entire construction and operation phase of a major port. It is extremely essential that port and harbour projects should have an environmental management plan (EMP), which also incorporates monitoring of air and marine water quality along with the collection of online meteorological data throughout the life of the project. This paper presents the environmental impacts due to various port activities and their sources and also discusses the EMP for different pollution prevention, protection and control measures.     

浅谈净化室内空气的方法

目前室内环境污染已成为影响人们健康的“隐形”杀手。本文介绍了室内空气污染物对人体的危害,对多种空气净化技术的优缺点进行了阐述,并对室内不同的污染物选择与之相应的空气净化设备给出了建议。     

STATISTICS OF CONTINUOUS PARTICULATE MEASUREMENT

This paper describes a methodology which estimates the average particulate concentration in a process gas of continuous rather than batch collected data. The method combines the statistical approach described by VanSlooten (1) and the use of a sliding average to the analysis of incoming continuous particle count data.

Standard deviation equations estimating the average particulate concentration in process gas streams as a function of the sample volume are derived, allowing calculation of the method's resolution for different sliding average window widths. The paper includes examples of the method applied to synthetic data, and discusses the effect of counter background, counter sampling rates, and window widths on the sliding average. In addition, continuous data from several facilities are analyzed by this method and the results are discussed.     

STATISTICS OF CONTINUOUS PARTICULATE MEASUREMENT

This paper describes a methodology which estimates the average particulate concentration in a process gas of continuous rather than batch collected data. The method combines the statistical approach described by VanSlooten (1) and the use of a sliding average to the analysis of incoming continuous particle count data.

Standard deviation equations estimating the average particulate concentration in process gas streams as a function of the sample volume are derived, allowing calculation of the method's resolution for different sliding average window widths. The paper includes examples of the method applied to synthetic data, and discusses the effect of counter background, counter sampling rates, and window widths on the sliding average. In addition, continuous data from several facilities are analyzed by this method and the results are discussed.