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 lm(PM2.5)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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 concentrations and has better performance than the benchmark models.

VARIATION CHARACTERISTICS OF THE PLANETARY BOUNDARY LAYER HEIGHT AND ITS RELATIONSHIP WITH PM2.5 CONCENTRATION OVER CHINA

The planetary boundary layer height(PBLH) was calculated using the radiosonde sounding data, including120 L-band operational sites and 8 GPS sites in China. The diurnal and seasonal variations of PBLH were analyzed using radiosonde sounding(OBS-PBLH) and ERA data(ERA-PBLH). Based on comparison and error analyses, we discussed the main error sources in these data. The frequency distributions of PBLH variations under different regimes(the convective boundary layer, the neutral residual layer, and the stable boundary layer) can be well fitted by a Gamma distribution and the shape parameter k and scale parameter s values were obtained for different regions of China. The variation characteristics of PBLH were found in summer under these three regimes for different regions. The relationships between PBLH and PM_(2.5) concentration generally follow a power law under very low or no precipitation conditions in the region of Beijing, Tianjin and Hebei in summer. The results usually deviated from this power distribution only under strong precipitation or high relative humidity conditions because of the effects of hygroscopic growth of aerosols or wet deposition. The OBS-PBLH provided a reasonable spatial distribution relative to ERA-PBLH.This indicates that OBS-PBLH has the potential for identifying the variation of PM_(2.5) concentration.

Analysis on Thermal Environment of Underlying Surface and PM2.5 Concentration in Community Park of Beijing in Winter

Community park is one of the most important landscape spaces for urban people to live outdoors,and people’s perception of environmental microclimate is a direct factor affecting the use frequency and experience of community parks.In this paper,Shijingshan Sculpture Park of Beijing was taken as experimental object.Using the method of fi eld measurement,9-d winter test for 3 months was conducted in three kinds of landscape architecture spaces,including waterfront plaza,open green space and square under the forest.Via regression analysis method,the measured air temperature(Ta),relative humidity of air(RH),particulate matter(PM2.5)were analyzed.It is found that winter sunshine is main infl uence factor of garden microclimate,and there is a negative correlation between local temperature and humidity;local temperature and humidity can regulate the local PM2.5 concentration,and temperature shows negative correlation with PM2.5 concentration,while humidity shows positive correlation with PM2.5 concentration.Meanwhile,via comparative analysis of temperature,humidity and PM2.5 concentration in different types of garden spaces,the infl uence of different space forms,planting forms and materials on thermal environment of underlying surface and PM2.5 concentration was summarized,and design strategy was optimized,to be as benefi cial reference of reconstruction design of community parks.

A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring

Nowadays,air pollution is a big environmental problem in develop-ing countries.In this problem,particulate matter 2.5(PM2.5)in the air is an air pollutant.When its concentration in the air is high in developing countries like Vietnam,it will harm everyone’s health.Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen.This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City,Vietnam.Firstly,this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC dataset.Only variables that affect the results will be selected for PM2.5 concentration prediction.Secondly,an efficient PM25-CBL model that integrates a convolutional neural network(CNN)andBidirectionalLongShort-TermMemory(Bi-LSTM)isdeveloped.This model consists of three following modules:CNN,Bi-LSTM,and Fully connected modules.Finally,this study conducts the experiment to compare the performance of our approach and several state-of-the-art deep learning models for time series prediction such as LSTM,Bi-LSTM,the combination of CNN and LSTM(CNN-LSTM),and ARIMA.The empirical results confirm that PM25-CBL model outperforms other methods for Air Quality HCMC dataset in terms of several metrics including Mean Squared Error(MSE),Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).

Multi-Scale Variation Prediction of PM2.5 Concentration Based on a Monte Carlo Method

Haze concentration prediction,especially PM2.5,has always been a significant focus of air quality research,which is necessary to start a deep study.Aimed at predicting the monthly average concentration of PM2.5 in Beijing,a novel method based on Monte Carlo model is conducted.In order to fully exploit the value of PM2.5 data,we take logarithmic processing of the original PM2.5 data and propose two different scales of the daily concentration and the daily chain development speed of PM2.5 respectively.The results show that these data are both approximately normal distribution.On the basis of the results,a Monte Carlo method can be applied to establish a probability model of normal distribution based on two different variables and random sampling numbers can also be generated by computer.Through a large number of simulation experiments,the average monthly concentration of PM2.5 in Beijing and the general trend of PM2.5 can be obtained.By comparing the errors between the real data and the predicted data,the Monte Carlo method is reliable in predicting the PM2.5 monthly mean concentration in the area.This study also provides a feasible method that may be applied in other studies to predict other pollutants with large scale time series data.

Analysis of PM2.5 concentrations in Heilongjiang Province associated with forest cover and other factors

Atmospheric particulate matter(PM2.5) seriously influences air quality. It is considered one of the main environmental triggers for lung and heart diseases. Air pollutants can be adsorbed by forest. In this study we investigated the effect of forest cover on urban PM2.5 concentrations in 12 cities in Heilongjiang Province,China. The forest cover in each city was constant throughout the study period. The average daily concentration of PM2.5 in 12 cities was below 75 lg/m^3 during the non-heating period but exceeded this level during heating period. Furthermore, there were more moderate pollution days in six cities. This indicated that forests had the ability to reduce the concentration of PM2.5 but the main cause of air pollution was excessive human interference and artificial heating in winter. We classified the 12 cities according to the average PM2.5 concentrations. The relationship between PM2.5 concentrations and forest cover was obtained by integrating forest cover, land area,heated areas and number of vehicles in cities. Finally,considering the complexity of PM2.5 formation and based on the theory of random forestry, we selected six cities and analyzed their meteorological and air pollutant data. The main factors affecting PM2.5 concentrations were PM10,NO_2, CO and SO_2 in air pollutants while meteorological factors were secondary.

Comparison of Ozone and PM2.5 Concentrations over Urban,Suburban,and Background Sites in China

Surface ozone(O3)and fine particulate matter(PM2.5)are dominant air pollutants in China.Concentrations of these pollutants can show significant differences between urban and nonurban areas.However,such contrast has never been explored on the country level.This study investigates the spatiotemporal characteristics of urban-to-suburban and urban-tobackground difference for O3(Δ[O3])and PM2.5(Δ[PM2.5])concentrations in China using monitoring data from 1171 urban,110 suburban,and 15 background sites built by the China National Environmental Monitoring Center(CNEMC).On the annual mean basis,the urban-to-suburbanΔ[O3]is−3.7 ppbv in Beijing-Tianjin-Hebei,1.0 ppbv in the Yangtze River Delta,−3.5 ppbv in the Pearl River Delta,and−3.8 ppbv in the Sichuan Basin.On the contrary,the urban-to-suburbanΔ[PM2.5]is 15.8,−0.3,3.5 and 2.4μg m^−3 in those areas,respectively.The urban-to-suburban contrast is more significant in winter for bothΔ[O3]andΔ[PM2.5].In eastern China,urban-to-background differences are also moderate during summer,with−5.1 to 6.8 ppbv forΔ[O3]and−0.1 to 22.5μg m^−3 forΔ[PM2.5].However,such contrasts are much larger in winter,with−22.2 to 5.5 ppbv forΔ[O3]and 3.1 to 82.3μg m^−3 forΔ[PM2.5].Since the urban region accounts for only 2%of the whole country’s area,the urban-dominant air quality data from the CNEMC network may overestimate winter[PM2.5]but underestimate winter[O3]over the vast domain of China.The study suggests that the CNEMC monitoring data should be used with caution for evaluating chemical models and assessing ecosystem health,which require more data outside urban areas.

The Regression Analysis between the Meteorological Synthetic Index Sequence and PM2.5 Concentration

Adapting daily meteorological data provided by China International Exchange Station, and using principal component analysis of meteorological index for dimension reduction comprehensive, the regression analysis model between PM2.5 and comprehensive index is established, by making use of Eviews time series modeling of the comprehensive principal component, finally puts forward opinions and suggestions aim at the regression analysis results of using artificial rainfall to ease haze.

基于改进基于改进Seq2Seq模型的华东地区模型的华东地区PM2.5预测预测

PM2.5数据是一种时间序列数据,具有较强的时序性与非线性特征。传统的时间序列模型算法有长短期记忆人工神经网络(LSTM)、循环神经网络(RNN)、编码器-解码器神经网络(Seq2Seq)等方法。本文提出一种基于Seq2Seq网络并融合注意力机制的PM2.5预测算法(Seq2Seq+Attention),其中Seq2Seq的cell单元为LSTM,能充分提取输入的有效特征信息,增强网络的学习能力和预测效果。利用2019年1月至2021年8月华东地区10个城市的PM2.5数据进行了预测试验,试验对比了LSTM、Seq2Seq和Seq2Seq+Attention3种方法在24h内的PM2.5数值预报准确度。研究结果表明,Seq2Seq+Attention方法在预测效果上优于其他方法,且24h的预测评分也高于其他方法。

基于PM2.5监测数据的大气污染防治措施改进研究

随着工业化和城市化的快速发展,大气污染已成为全球性的环境问题。PM2.5作为主要的大气污染物之一,对人体健康和生态环境构成了严重威胁。因此,有效防治PM2.5污染,已成为当前环境保护工作的重点。基于PM2.5监测数据,探讨大气污染防治措施的改进方法。通过对现有监测数据的深入分析,研究识别了污染源的主要类型和时空分布特征,评估了现行防治措施的效果,并提出了针对性的改进建议。基于此,本篇文章对基于PM2.5监测数据的大气污染防治措施改进进行研究,以供参考。

Estimation of daily PM2.5 concentration and its relationship with meteorological conditions in Beijing

When investigating the impact of air pollution on health, particulate matter less than 2.5μm in aerodynamic diameter （PM2.5） is considered more harrnful than particulates of other sizes. Therefore, studies of PM2.5 have attracted more attention. Beijing, the capital of China, is notorious for its serious air pollution problem, an issue which has been of great concern to the residents, government, and related institutes for decades. However, in China, significantly less time has been devoted to observing PM2.5 than for PM10. Especially before 2013, the density of the PM2.5 ground observation network was relatively low, and the distribution of observation stations was uneven. One solution is to estimate PM2.5 concentrations from the existing data on PM10. In the present study, by analyzing the relationship between the concentrations of PM2.5 and PM10, and the meteorological conditions for each season in Beijing from 2008 to 2014, a U-shaped relationship was found between the daily maximum wind speed and the daily PM concentration, including both PM2.5 and PM10. That is, the relationship between wind speed and PM concentration is not a simple positive or negative correlation in these wind directions; their relationship has a complex effect, with higher PM at low and high wind than for moderate winds. Additionally, in contrast to previous studies, we found that the PM2.5/PM10 ratio is proportional to the mean relative humidity （MRH）. According to this relationship, for each season we established a multiple nonlinear regression （MNLR） model to estimate the PM2.5 concentrations of the missing periods.

地区空气污染的“压力”与企业绿色转型的“动力”——基于城市PM2.5和公司并购的实证发现

地区空气污染会影响域内各类市场主体行为,包括微观企业决策。碳达峰、碳中和的提出要求各地加强空气污染治理,推动企业绿色转型,而绿色并购是企业绿色转型的重要方式。利用2013—2018年我国A股上市的污染企业发起的并购为样本,并手动搜集整理确定绿色并购样本,实证研究地区空气污染对域内企业绿色转型的影响。研究发现,地区空气污染加剧会提升域内污染企业绿色并购的意愿。在此基础上,通过并购前后企业信息披露质量和社会责任承担变化,研究发现地区空气污染加剧下的企业绿色转型并非源于自主绿色转型发展的内生“动力”,更多是外部“压力”下的“工具主义”行为。从影响路径来看,地区空气污染引致的压力会通过行业竞争和融资约束这两个途径影响污染企业绿色转型的“动力”。最后,异质性检验发现在市场化程度高和非国有企业样本,地区空气污染加剧对企业绿色并购的促进作用更为显著。

PM2.5 concentration estimation using convolutional neural network and gradient boosting machine

Surface monitoring, vertical atmospheric column observation, and simulation using chemical transportation models are three dominant approaches for perception of fine particles with diameters less than 2.5 micrometers(PM2.5) concentration. Here we explored an imagebased methodology with a deep learning approach and machine learning approach to extend the ability on PM2.5 perception. Using 6976 images combined with daily weather conditions and hourly time data in Shanghai(2016), trained by hourly surface monitoring concentrations, an end-to-end model consisting of convolutional neural network and gradient boosting machine(GBM) was constructed. The mean absolute error, the root-mean-square error and the R-squared for PM2.5 concentration estimation using our proposed method is 3.56, 10.02, and 0.85 respectively. The transferability analysis showed that networks trained in Shanghai, fine-tuned with only 10% of images in other locations, achieved performances similar to ones from trained on data from target locations themselves. The sensitivity of different regions in the image to PM2.5 concentration was also quantified through the analysis of feature importance in GBM. All the required inputs in this study are commonly available, which greatly improved the accessibility of PM2.5 concentration for placed and period with no surface observation. And this study makes an exploratory attempt on pollution monitoring using graph theory and deep learning approach.

Quantitative relationship between visibility and mass concentration of PM2.5 in Beijing

The pollution of particulate matter less than 2.5μm （PM2.5） is a serious environmental problem in Beijing. The annual average concentration of PM2.5 in 2001 from seasonal monitor results was more than 6 times that of the U,S, national ambient air quality standards proposed by U.S. EPA. The major contributors to mass of PM2.5 were organics, crustal elements and sulfate. The chemical composition of PM2.5 varied largely with season, but was similar at different monitor stations in the same season. The fine particles （PM2.5） cause atmospheric visibility deterioration through light extinction, The mass concentrations of PM2.5 were anti-correlated to the visibility, the best fits between atmospheric visibility and the mass concentrations of PM2.5 were somehow different： power in spring, exponential in summer, logarithmic in autumn, power or exponential in winter. As in each season the meteorological parameters such as air temperature and relative humidity change from day to day, probably the reason of above correlations between PM2.5 and visibility obtained at different seasons come from the differences in chemical compositions of PM2.5.

Impact of Urban 3D Morphology on Particulate Matter 2.5(PM2.5) Concentrations:Case Study of Beijing, China

Urban particulate matter 2.5(PM2.5)pollution and public health are closely related,and concerns regarding PM2.5 are widespread.Of the underlying factors,the urban morphology is the most manageable.Therefore,investigations of the impact of urban three-dimensional(3D)morphology on PM2.5 concentration have important scientific significance.In this paper,39 PM2.5 monitoring sites of Beijing in China were selected with PM2.5 automatic monitoring data that were collected in 2013.This data set was used to analyze the impacts of the meteorological condition and public transportation on PM2.5 concentrations.Based on the elimination of the meteorological conditions and public transportation factors,the relationships between urban 3D morphology and PM2.5 concentrations are highlighted.Ten urban 3D morphology indices were established to explore the spatial-temporal correlations between the indices and PM2.5 concentrations and analyze the impact of urban 3D morphology on the PM2.5 concentrations.Results demonstrated that road length density(RLD),road area density(RAD),construction area density(CAD),construction height density(CHD),construction volume density(CVD),construction otherness(CO),and vegetation area density(VAD)have positive impacts on the PM2.5 concentrations,whereas water area density(WAD),water fragmentation(WF),and vegetation fragmentation(VF)(except for the 500 m buffer)have negative impacts on the PM2.5 concentrations.Moreover,the correlations between the morphology indices and PM2.5 concentrations varied with the buffer scale.The findings could lay a foundation for the high-precision spatial-temporal modelling of PM2.5 concentrations and the scientific planning of urban 3D spaces by authorities responsible for controlling PM2.5 concentrations.

Oscillation of Surface PM2.5 Concentration Resulting from an Alternation of Easterly and Southerly Winds in Beijing： Mechanisms and Implications

We used simultaneous measurements of surface PM_（2.5） concentration and vertical profiles of aerosol concentration,temperature, and humidity, together with regional air quality model simulations, to study an episode of aerosol pollution in Beijing from 15 to 19 November 2016. The potential effects of easterly and southerly winds on the surface concentrations and vertical profiles of the PM_（2.5） pollution were investigated. Favorable easterly winds produced strong upward motion and were able to transport the PM_（2.5） pollution at the surface to the upper levels of the atmosphere. The amount of surface PM_（2.5） pollution transported by the easterly winds was determined by the strength and height of the upward motion produced by the easterly winds and the initial height of the upward wind. A greater amount of PM_（2.5） pollution was transported to upper levels of the atmosphere by upward winds with a lower initial height. The pollutants were diluted by easterly winds from clean ocean air masses. The inversion layer was destroyed by the easterly winds and the surface pollutants and warm air masses were then lifted to the upper levels of the atmosphere, where they re-established a multi-layer inversion. This region of inversion was strengthened by the southerly winds, increasing the severity of pollution. A vortex was produced by southerly winds that led to the convergence of air along the Taihang Mountains. Pollutants were transported from southern–central Hebei Province to Beijing in the boundary layer. Warm advection associated with the southerly winds intensified the inversion produced by the easterly winds and a more stable boundary layer was formed. The layer with high PM_（2.5） concentration became dee-per with persistent southerly winds of a certain depth. The polluted air masses then rose over the northern Taihang Mountains to the northern mountainous regions of Hebei Province.

Exploration of spatial and temporal characteristics of PM2.5 concentration in Guangzhou, China using wavelet analysis and modified land use regression model

This article attempts to detail time series characteristics of PM2.5 concentration in Guangzhou(China)from 1 June 2012 to 31 May 2013 based on wavelet analysis tools,and discuss its spatial distribution using geographic information system software and a modified land use regression model.In this modified model,an important variable(land use data)is substituted for impervious surface area,which can be obtained conveniently from remote sensing imagery through the linear spectral mixture analysis method.Impervious surface has higher precision than land use data because of its sub-pixel level.Seasonal concentration pattern and day-by-day change feature of PM2.5 in Guangzhou with a micro-perspective are discussed and understood.Results include:(1)the highest concentration of PM2.5 occurs in October and the lowest in July,respectively;(2)average concentration of PM2.5 in winter is higher than in other seasons;and(3)there are two high concentration zones in winter and one zone in spring.

基于UPLC-Q-TOF MS的染毒(PM2.5)大鼠尿液代谢组学研究

为探明PM2.5对大鼠代谢组学的影响,将SD大鼠随机分为对照组(SC)和PM2.5模型组(MG),每组15只。将生理盐水和PM2.5混悬液分别注入各组大鼠气管,每周2次,持续4周。通过超高效液相色谱-四极杆-飞行时间质谱(UPLC-Q-TOF MS)对各组大鼠尿液的代谢组学变化进行检测,采用多元统计分析探究整体代谢组学的变化。通过数据分析和数据库检索,从大鼠尿样中鉴定出17种潜在生物标记物,主要代谢途径涉及戊糖和葡萄糖醛酸相互转化、色氨酸代谢、酪氨酸代谢、苯丙氨酸代谢、嘌呤代谢、对乙酰氨基酚代谢、视黄醇代谢和丙戊酸代谢途径,PM2.5对大鼠诱导损伤作用,可能与其扰乱大鼠体内正常代谢活动有关。研究结果有助于了解PM2.5的毒理学机制,筛选PM2.5暴露大鼠的潜在生物标志物,为进一步探索PM2.5毒性作用及致病机制提供了理论依据。

How aerosol direct effects influence the source contributions to PM2.5 concentrations over Southern Hebei, China in severe winter haze episodes

Beijing-Tianjin-Hebei area is the most air polluted region in China and the three neighborhood southern Hebei cities, Shijiazhuang, Xingtai, and Handan, are listed in the top ten polluted cities with severe PM2.5 pollution. The objective of this paper is to evaluate the impacts of aerosol direct effects on air aualitv over the southern Hebei cities, as well as the im0acts when considering those effects on source apportionment using three dimensional air quality models. The WRF/Chem model was applied over the East Asia and northern China at 36 and 12 km horizontal grid resolutions, respectively, for the period of January 2013, with two sets of simulations with or without aerosol-meteorology feedbacks. The source contributions of power plants, industrial, domestic, transportation, and agriculture are evaluated using the Brute-Force Method （BFM） under the two simulation configurations. Our results indicate that, although the increases in PM2.5 concentrations due to those effects over the three southern Hebei cities are only 3%-9% on monthly average they are much more significant under high PM2.5 Ioadmgs （-50 gg.m - when PM25 concentrations are higher than 400μg.m^-3）. When considering the aerosol feedbacks, the contributions of industrial and domestic sources assessed using the BFM will obviously increase （e.g., from 30% 34% to 32%-37% for industrial）, especiallY3under high PM2.5 loadings （e.g., from 36%-44% to 43%-47% for domestic when PM2.5〉400μg·m^-3）. Our results imply that the aerosol direct effects should not be ignored during severe pollution episodes, especially in short-term source apportionment using the BFM.

PM2.5诱导NLRP3炎症小体活化对人主动脉内皮细胞的影响

目的 探讨PM2.5诱导人主动脉内皮细胞(HAECs)损伤的分子机制。方法 收集大气PM2.5染毒HAECs 24 h,采用MTT法检测细胞存活率,酶联免疫吸附实验检测(ELISA)白介素-1β(IL-1β)和IL-18的含量,硫代巴比妥法检测丙二醛(MDA)含量,比色法检测乳酸脱氢酶(LDH)活性,Western blot和Q-PCR法检测NLRP3、caspase-1、IL-1β、Bax和Bcl-2的表达,流式细胞术和DAPI染色检测细胞凋亡,活性氧(ROS)和线粒体ROS(mtROS)试剂盒检测ROS和mtROS水平,使用NLRP3 siRNA及ROS和mtROS特异性抑制剂(NAC和Mito-TEMPO)后,观察上述结果的变化。结果 PM2.5可引起HAECs细胞分泌IL-1β和IL-18增加,释放MDA和LDH增多,促进细胞凋亡,并呈现剂量依赖关系;PM2.5可诱导HAECs细胞caspase-1和IL-1β表达增加,还可使HAECs细胞的ROS和mtROS水平显著升高;使用NLRP3 siRNA以及ROS和mtROS的抑制剂可明显抑制上述效应。结论 PM2.5通过诱导HAECs细胞氧化应激而活化NLRP3炎症小体,进一步引起细胞炎性反应和凋亡。