保肺化纤方调控TGF-β1/smad2/3、Wnt1/β-catenin信号通路对BLM/PM2.5诱导的特发性肺纤维化大鼠肺组织胶原沉积的影响

目的探讨保肺化纤方经转化生长因子(transforming growth factor,TGF)-β1/smad2/3、Wnt1/β-连环蛋白(catenin)信号通路减轻博来霉素(bleomycin,BLM)/PM2.5暴露诱导的特发性肺纤维化(idiopathic pulmonary fibrosis,IPF)大鼠肺组织胶原沉积的作用机制。方法SD大鼠随机分为对照(Control)组、BLM+PM2.5(Model)组、保肺化纤方(BHF)组、吡非尼酮(pirfenidone,PFD)组、XAV-939组、BHF+PFD组、BHF+XAV-939组。采用一次性气管内雾化BLM法制备IPF大鼠模型,大气PM2.5实时浓缩全身暴露和给予相应药物干预。检测大鼠肺功能,观察肺组织病理和胶原沉积;采用免疫组化技术检测肺组织Ⅰ、Ⅳ型胶原(collagen,COL)、TGF-β1、smad2/3、β-catenin蛋白水平;采用qPCR技术检测肺组织TGF-β1、smad2/3、Wnt1、β-catenin mRNA水平。结果与Control组比较,Model组大鼠肺功能显著降低(P<0.01),肺组织可见大量炎症细胞浸润,肺泡壁断裂、增厚,胶原沉积等肺纤维化特征性病理改变,肺组织胶原容积分数(collagen volume fraction,CVF)、COLⅠ、COLⅣ蛋白及TGF-β1、smad2/3、β-catenin基因与蛋白表达均显著升高(P<0.01);与Model组比较,各治疗组均有效改善大鼠肺组织病理改变,显著降低肺组织CVF及COLⅠ、COLⅣ、TGF-β1、smad2/3、β-catenin蛋白表达水平(P<0.05,P<0.01),同时BHF组、BHF+PFD组、BHF+XAV-939组均能显著提高大鼠肺功能(P<0.05,P<0.01),明显下调肺组织TGF-β1、smad2/3、β-catenin基因表达水平(P<0.01);与PFD组比较,BHF+PFD组大鼠肺组织COLⅠ、TGF-β1蛋白及smad2、β-catenin基因表达均显著降低(P<0.05,P<0.01);与XAV-939组比较,BHF+XAV-939组smad3基因表达显著降低(P<0.01)。结论保肺化纤方可改善BLM/PM2.5诱导的IPF大鼠肺组织病理损伤,减少胶原沉积,提高肺功能,改善肺纤维化,其机制可能与下调TGF-β1/smad2/3、Wnt1/β-catenin通路有关。

Spatial and Temporal Variation of Particulate Matter (PM10 and PM2.5) and Its Health Effects during the Haze Event in Malaysia

This study aims to assess and compare levels of particulate matter(PM10 and PM2.5)in urban and industrial areas in Malaysia during haze episodes,which typically occur in the south west monsoon season.The high concentrations of atmospheric particles are mainly due to pollution from neighbouring countries.Daily PM concentrations were analysed for urban and industrial areas including Alor Setar,Tasek,Shah Alam,Klang,Bandaraya Melaka,Larkin,Balok Baru,and Kuala Terengganu in 2018 and 2019.The analysis employed spatiotemporal to examine how PM levels were distributed.The data summary revealed that PM levels in all study areas were right-skewed,indicating the occurrence of high particulate events.Significant peaks in PM concentrations during haze events were consistently observed between June and October,encompassing the south west monsoon and inter-monsoon periods.The study on acute respiratory illnesses primarily focused on Selangor.Analysis revealed that Klang had the highest mean number of inpatient cases for acute exacerbation of bronchial asthma(AEBA)and acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with values of 260.500 and 185.170,respectively.Similarly,for outpatient cases of AEBA and AECOPD,Klang had the highest average values of 41.67 and 14.00,respectively.Shah Alam and Sungai Buloh did not show a significant increase in cases during periods of biomass burning.The statistical analysis concluded that higher concentrations of PM were associated with increased hospital admissions,particularly from June to September,as shown in the bar diagram.Haze episodes were associated with more healthcare utilization due to haze-related respiratory illnesses,seen in higher inpatient and outpatient visits(p<0.05).However,seasonal variability had minimal impact on healthcare utilization.These findings offer a comprehensive assessment of PM levels during historic haze episodes,providing valuable insights for authorities to develop policies and guidelines for effective monitoring and mitigation of the negative impacts of haze events.

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。

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。

Polychlorinated biphenyls in the atmosphere of Taizhou,a major e-waste dismantling area in China

PM2.5, total suspended particles （TSP） and gas phase samples were collected at two sites of Talzhou, a major e-waste dismantling area in China. Concentrations, seasonal variations, congener profiles, gas-particle partitioning and size distribution of the atmospheric polychlorinated biphenyls （PCBs） were studied to assess the current state of atmospheric PCBs after the phase out of massive historical dismantling of PCBs containing e-wastes. The average ∑38PCBs concentration in the ambient air （TSP plus gas phase） near the e-waste dismantling area was （12,407 ± 9592） pg/m^3 in winter, which was substantially lower than that found one decade ago. However, the atmospheric PCBs level near the e-waste dismantling area was 54 times of the reference urban site, indicating that the impact of the historical dismantling of PCBs containing e-wastes was still significant. Tri-Penta-CBs were dominant homologues, consisting with their dominant global production. Size distribution of particle-bound PCBs showed that higher chlorinated CBs tended to partition more to the fine particles, facilitating its long range air transportation.

PM2.5 and tropospheric O_3 in China and an analysis of the impact of pollutant emission control

This study reviewed the status of PM2.5 and tropospheric O3 observations in China(15e55N, 72e136E). Initially, the distribution of tropospheric O3 over the globe and China was determined based on satellite observations made during 2010e2013. The annual mean values were 29.78 DU and 33.97 DU over the globe and China, respectively. The distribution of PM2.5 and seasonal changes in concentrations in China were then simulated using an aerosol chemistry e climate coupled model system, with an annual mean value of 0.51×10-8kg mà3. The contributions from five different aerosols to the simulated PM2.5 concentrations in different seasons were also determined. The relationships among the emissions of aerosols, greenhouse gases and their precursors and radiative forcings were determined with reference to the(IPCC AR5) Intergovernmental Panel on Climate Change the Fifth Assessment Report. From these relationships, the possible effects of controlling O3 precursors and(PM) particulate matter on the climate were considered. The influence of the control of O3 precursors was not totally clear, and reducing emissions of short-lived greenhouse gases and black carbon was considered a secondary measure for short-term(the next 50years) climate-change mitigation. Reducing emissions of CO2 is still the best strategy for meeting the target of a global average rise in surface air temperature of less than 2C. Near- and short-term emission reduction strategies are important for both effective environmental protection and climate-change mitigation.

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.

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。

景观防护林带PM2.5与环境因子的关系研究

以长江沿岸的典型景观防护林为研究对象,选取榔榆、乌桕、落羽杉和薄壳山核桃4种纯林类型的景观防护林,于2023年监测PM2.5、风速、风向、光照度、空气湿度、空气温度、气压、土壤温度和土壤湿度的季度变化。发现乌桕防护林带PM2.5质量浓度变化幅度较大,夏季明显低于冬季,而其他3个防护林带PM2.5质量浓度季节变化幅度不大。通过相关性分析发现PM2.5与多个环境因子有相关性,尤其是乌桕防护林带。为了探究各环境因子对PM2.5的影响,该研究引入随机森林模型,结果表明在长江沿岸的典型景观防护林中空气湿度是影响PM2.5质量浓度的主要环境因子。该研究结果为未来建构科学合理的防护林体系提供了理论依据。

Spatial and Temporal Variations in the Atmospheric Age Distribution of Primary and Secondary Inorganic Aerosols in China

The aging timescale of particles is a key parameter in determining their impacts on air quality,human health,and climate.In this study,a one-year simulation of the age distributions of the primary and secondary inorganic fine particulate matter(PM_(2.5))components was conducted over China using an age-resolved Community Multiscale Air Quality(CMAQ)model.The results indicate that primary PM_(2.5)(PPM)and ammonium mainly originate from fresh local emissions,with approximately 60%-80%concentrated in 0-24 h age bins in most of China throughout the year.The average age is about15-25 h in most regions in summer,but increases to 40-50 h in southern region of China and the Sichuan Basin(SCB)in winter.Sulfate is more aged than PPM,indicating an enhanced contribution from regional transport.Aged sulfate with atmospheric age>48 h account for 30%-50%of total sulfate in most regions and seasons,and the concentrations in the>96 h age bin can reach up to 15μg·m-3 in SCB during winter.Dramatic seasonal variations occur in the Yangtze River Delta,Pearl River Delta,and SCB,with highest average age of 60-70 h in winter and lowest of 40-45 h in summer.The average age of nitrate is 20-30 h in summer and increases to 40-50 h in winter.The enhanced deposition rate of nitric acid vapor combined with the faster chemical reaction rate of nitrogen oxides leads to a lower atmospheric age in summer.Additionally,on pollution days,the contributions of old age bins(>24 h)increase notably for both PPM and secondary inorganic aerosols in most cities and seasons,suggesting that regional transport plays a vital role during haze events.The age information of PM_(2.5),provided by the age-resolved CMAQ model,can help policymakers design effective emergent emission control measures to eliminate severe haze episodes.

基于MSPA分析绿地形态空间格局对PM2.5的消退作用——以广州市、深圳市为例

在土地资源紧张的背景下,优化绿地形态空间格局对提升空气质量具有重要的意义。以往相关研究多采用景观格局指数衡量绿地空间特征,据此研究不同的绿地景观形态对PM2.5的消退作用,但景观格局指数只是反映绿地数量与结构的量化指标,无法落实到具体空间上。为了解决这一不足,以广州市、深圳市为例,基于MSPA探究不同的绿地形态空间格局对PM2.5的消退作用。结果表明:21个研究单元的PM2.5值在时间上变化基本一致,在空间上的高低与附近是否有绿地具有紧密的关系;相关分析中, PM2.5与建设用地面积、孤岛占比呈显著正相关,与核心区占比呈显著负相关;随机森林模型中,分支占比、孤岛占比、道路密度对PM2.5浓度的影响效果较强;市区的PM2.5受核心区占比、孤岛占比的影响较大,而郊区的PM2.5受不同的绿地形态空间格局影响不大。根据分析结果,提出加强保护大型绿色开放空间;整合破碎化绿地,使孤岛绿地形态核心化;构建线状城市绿廊绿道的策略改善PM2.5的污染。基于MSPA揭示了不同绿地形态空间格局对PM2.5的消退作用的机制,所提出的相关策略可以为大气污染防治和城市绿地空间格局的规划管理提供新思路。

融合时空特征的城市多站点PM2.5浓度预测

本文提出一种融合时空特征的城市多站点PM2.5预测方法,该方法可以捕捉PM2.5在时间和空间上的相关性,通过将区域多个站点的PM2.5数据转换为一系列静态图像,将其输入到卷积长短期记忆(ConvLSTM)模型中,采用端对端的方式进行训练,预测城市未来多个站点多个时段的PM2.5浓度。以北京多个站点的PM2.5数据进行实验验证。结果表明:考虑了时空特征的ConvLSTM方法在短期预测方面优于其他4种时序方法,该方法可为PM2.5预测提供新的思路。

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.

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).

Effects of Combined Exposure to Formaldehyde and PM2.5 on the Brain of Balb/c Mice Asthma Model

Epidemiological studies have shown that there is a link between asthma and brain damage,but toxicological studies have not fully confirmed yet,especially the effects of asthma on the brain. In this study,at first,we explore the effects of asthma on the brain through the establishment of an allergic asthma model. Then PM_(2.5),a typical outdoor air pollutant and formaldehyde,a typical indoor air pollutant were selected to be closer to the true environment and find whether there is any synergism between them. In this study,an ovalbumin( OVA)-sensitized mice asthma model was established. 30 male Balb/c mice were randomly divided into 5 groups:( 1) saline control group,( 2) OVA-sensitized group,( 3) OVA-combined with formaldehyde exposure group,( 4) OVA-combined with PM_(2.5) exposure group,( 5) Combination of OVA,formaldehyde and PM_(2.5) exposure group. The mice were inhaled with formaldehyde or/and instilled with PM_(2.5) from day 1 to 18. The mice asthma model was developed by OVA sensitization and challenge. The mice were sensitized with OVA+Al( OH)3( 5 mg OVA and 175 mg Al( OH)3 in 30 m L saline each time) or saline( 30 m L saline each time) by intraperitoneal injection on day 1,7 and 14.This was then followed by an aerosol challenge in 1% OVA( 30 min·d^(-1)) from day 19 to 25( 7 times) using an ultrasonic nebulizer. On the 26 th day,the organ coefficient of mice brain was counted,then the contents of oxidative stress of mice brain were measured,including reactive oxygen species( ROS),glutathione( GSH) and malondialdehyde( MDA),and the concentrations of NF-κB and interleukin-1β( IL-1β) were detected by using ELISA kits.Detection of interleukin-6( IL-6) was made with immunohistochemical method. Histological assay for brain was also conducted. In our results,all the OVA treated groups showed a significant increase of ROS and a significant decrease of GSH contents when compared with the control group. Except OVA-sensitized group,other OVA treated groups also showed a significant increase of MDA contents when compared with the control group,and MDA contents of OVA-sensitized group showed significant change when compared to the combined exposure group. In ROS and GSH,combined exposure showed some joint effect compared with single exposure. When OVA was applied in combination with formaldehyde and PM_(2.5),NF-κB was activated. And all the OVA treated groups showed increased levels of IL-1β and IL-6 compared with the control group. And the combined exposure showed an aggravated effect when compared with OVA-sensitized group. Histopathological observation of the hippocampus in mice brain clearly showed the difference of eosin( EO) stained neurons in the combined exposure group compared with the control group and OVA-sensitized group. The pyramidal neurons of the mice with allergic asthma exposed to formaldehyde and/or PM_(2.5) had been reduced in number,the cells were swollen and the dendrites had disappeared. Allergic asthma can cause damage to the brain through oxidative stress. Exposure to formaldehyde and PM_(2.5) will increase the damage caused by allergic asthma to the brain,which may be mediated by oxidative stress and NF-κB activation.This promotes the release of the inflammatory factors,resulting in increased inflammation.

Impact of meteorological conditions and PM2.5 on the onset of acute aortic dissection in monsoonal climate

Objective To investigate the impact of meteorological conditions and PM2.5 on the onset of acute aortic dissection in monsoonal climate. Methods A linear regression analysis was performed in monsoonal climate epidemiological survey for a period of four years on the impact of meteorological factors （minimal temperature, mean temperature, maximal temperature, average daily surface temperature, day temperature range, relative humidity, mean wind speed, and atmospheric pressure） and PM2.5 concentration on the daily incidences of acute aortic dissections. Meteorological variables and PM2.5 concentration were retrieved on a daily basis from Beijing Regional Climate Center and the Ministry of Environmental Protection of the People’s Republic of China’s website, and the daily incidences of acute aortic dissections were retrieved from the Clinical Data Analysis and Reporting System in the Emergency and Critical Care Center of Beijing Anzhen Hospital. Results During the study period （from January 2011 to December 2014）, 1164 patients were identified as having acute aortic dissections. The corresponding incidences in spring and autumn were 0.96 and 1.00, respectively, which significantly higher than that in summer and winter. The incidences of acute aortic dissection in a day could be predicted by diurnal temperature range （DTR） using the following linear multiple regression models： incidences of acute aortic dissection = 0.543 ＋ 0.025 × DTR. Conclusion This is the first study to show an attributable effect of DTR on acute aortic events in monsoonal climate. Our study confirms that meteorological variables were important factors influencing the incidence of acute aortic dissection.

基于Prophet-LightGBM的PM2.5浓度预测模型

近年来,PM2.5污染问题日益突出,对人们的身体健康和环境质量造成了严重影响,建立准确的PM2.5浓度预测模型对于污染防治和空气质量管理具有重要意义。针对PM2.5时间序列的非线性、高噪声、不平稳特征提出一种将Prophet模型和LightGBM模型相结合的组合模型。为了验证模型的有效性,以兰州市PM2.5浓度数据为例,对比分析了Prophet-LightGBM模型和其他4种预测模型及其在不同季节下的预测效果。结果表明,Prophet-LightGBM模型相较于对比模型能够更准确地预测PM2.5浓度的变化趋势,RMSE值达6.557,MAE值达4.543,MAPE值达14.344%,在夏季和秋季的预测准确度和稳定性方面表现出更优异的性能,RMSE值最优时达3.155,MAE值达2.169,MAPE值达9.4%。

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.

基于多元回归的延安市PM2.5浓度预测

工业化进程的加快带来的不只是经济的飞速发展,还有以PM2.5为主的污染物浓度的增加,给人类的健康以及环境的治理带来不利影响,合理有效的PM2.5浓度预测对于人类健康和环境治理有着重要意义。文中设计了基于多元回归模型的PM2.5浓度预测模型,分别预测了延安市春季、夏季、秋季和冬季的PM2.5浓度,与极限树回归、Catboost回归和K邻近回归等回归模型的预测结果进行对比。结果表明多元回归模型的误差较小,拟合精度较高,为延安市大气污染的治理提供了可靠的科学依据。

基于函数型数据模型的PM2.5浓度与温度关系的实证分析

考虑在函数型解释变量部分观测的情况下,用函数线性模型刻画与标量响应变量的关系.基于函数型主成分分析(Functional Principal Component Analysis,简称FPCA)实现了对缺失部分样本的重构,并通过实证分析,对一组北京市2010-2014年间统计的包括部分观测PM2.5数值的气象数据,分析了PM2.5作为部分观测函数型解释变量对标量响应变量平均气温的影响,结果表明了该方法具有处理缺失函数数据的现实意义.