A Time Series Analysis of Outdoor Air Pollution and Preterm Birth in Shanghai, China

Objective To investigate the relation between air pollution exposure and preterm birth in Shanghai, China. Methods We examined the effect of ambient air pollution on preterm birth using time-series approach in Shanghai in 2004. This method can eliminate potential confounding by individual risk factors that do not change over a short period of time. Daily numbers of preterm births were obtained from the live birth database maintained by Shanghai Municipal Center of Disease Control and Prevention. We used the generalized additive model （GAM） with penalized splines to analyze the relation between preterm birth, air pollution, and covariates. Results We observed a significant effect of outdoor air pollution only with 8-week exposure before preterm births. An increase of 10 μg/m^3 of 8-week average PM10, SO2, NO2, and O3 corresponded to 4.42% （95%CI 1.60%, 7.25%）, 11.89% （95%CI 6.69%, 17.09%）, 5.43% （95%CI 1.78%, 9.08%）, and 4.63% （95%CI 0.35%, 8.91%） increase of preterm birth. We did not find any significant acute effect of outdoor air pollution on preterm birth in the week before birth. Conclusion Ambient air pollution may contribute to the risk of preterm birth in Shanghai. Our analyses also strengthen the rationale for further limiting air pollution level in the city.

Association between Ambient Air Pollution and Hospital Emergency Admissions for Respiratory and Cardiovascular Diseases in Beijing: a Time Series Study

Objective To investigate the association between ambient air pollution and hospital emergency admissions in Beijing. Methods In this study, a semi-parametric generalized additive model （GAM） was used to evaluate the specific influences of air pollutants （PM10, SO2, and NO2） on hospital emergency admissions with different lag structures from 2009 to 2011, the sex and age specific influences of air pollution and the modifying effect of seasons on air pollution to analyze the possible interaction. Results It was found that a 10μg/m3 increase in concentration of PMlo at lag 03 day, SO2 and NO2 at lag 0 day were associated with an increase of 0.88%, 0.76%, and 1.82% respectively in overall emergency admissions. A 10 lag/m3 increase in concentration of PM10, SO2 and NO2 at lag 5 day were associated with an increase of 1.39%, 1.56%, and 1.18% respectively in cardiovascular disease emergency admissions. For lag 02, a 10 μg/m3 increase in concentration of PM10, SO2 and NO2 were associated with 1.72%, 1.34%, and 2.57% increases respectively in respiratory disease emergency admissions. Conclusion This study further confirmed that short-term exposure to ambient air pollution was associated with increased risk of hospital emergency admissions in Beijing.

Time Series Analysis and Forecasting of the Air Quality Index of Atmospheric Air Pollutants in Zahleh, Lebanon

During the last decades, air pollution has become a serious environmental hazard. Its impact on public health and safety, as well as on the ecosystem, has been dramatic. Forecasting the levels of air pollution to maintain the climatic conditions and environmental protection becomes crucial for government authorities to develop strategies for the prevention of pollution. This study aims to evaluate the atmospheric air pollution of the city of Zahleh located in the geographic zone of Bekaa. The study aims to determine a relationship between variations in ambient particulate concentrations during a short time. The data was collected from June 2017 to June 2018. In order to predict the Air Quality Index (AQI), Naïve, Exponential Smoothing, TBATS (a forecasting method to model time series data), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were implemented. The performance of these models for predicting air quality is measured using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Relative Error (RE). SARIMA model is the most accurate in prediction of AQI (RMSE = 38.04, MAE = 22.52 and RE = 0.16). The results reveal that SARIMA can be applied to cities like Zahleh to assess the level of air pollution and to prevent harmful impacts on health. Furthermore, the authorities responsible for controlling the air quality may use this model to measure the level of air pollution in the nearest future and establish a mechanism to identify the high peaks of air pollution.

Dynamic Ensemble Multivariate Time Series Forecasting Model for PM2.5

In forecasting real time environmental factors,large data is needed to analyse the pattern behind the data values.Air pollution is a major threat towards developing countries and it is proliferating every year.Many methods in time ser-ies prediction and deep learning models to estimate the severity of air pollution.Each independent variable contributing towards pollution is necessary to analyse the trend behind the air pollution in that particular locality.This approach selects multivariate time series and coalesce a real time updatable autoregressive model to forecast Particulate matter(PM)PM2.5.To perform experimental analysis the data from the Central Pollution Control Board(CPCB)is used.Prediction is car-ried out for Chennai with seven locations and estimated PM’s using the weighted ensemble method.Proposed method for air pollution prediction unveiled effective and moored performance in long term prediction.Dynamic budge with high weighted k-models are used simultaneously and devising an ensemble helps to achieve stable forecasting.Computational time of ensemble decreases with paral-lel processing in each sub model.Weighted ensemble model shows high perfor-mance in long term prediction when compared to the traditional time series models like Vector Auto-Regression(VAR),Autoregressive Integrated with Mov-ing Average(ARIMA),Autoregressive Moving Average with Extended terms(ARMEX).Evaluation metrics like Root Mean Square Error(RMSE),Mean Absolute Error(MAE)and the time to achieve the time series are compared.

Comparison of Air Pollution−Mortality Associations Using Observed Particulate Matter Concentrations and Reanalysis Data in 33 Spanish Cities

Air pollution poses a health hazard in all countries.However,complete data on ambient particulate matter(PM)concentrations are not available in all world regions.Reanalysis data is already a valuable source of exposure data in epidemiological studies examining the relationship between temperature and health.Nevertheless,the performance of reanalysis data in assessing the short-term health effects of particulate air pollution remains unclear.We assessed the performance of CAMS reanalysis(EAC4)data from the European Centre for Medium-Range Weather Forecasts,compared with daily PM concentrations from field monitoring stations,to estimate short-term exposure to PM with an aerodynamic diameter less than 10μm(PM_(10))on daily mortality in 33 Spanish provincial capital cities using a two-stage time series regression design.The shape of the PM_(10)distribution varied substantially between PM observations and CAMS global reanalysis of atmospheric composition(EAC4)reanalysis data,with correlation ranging from 0.21 to 0.58.The pooled mortality risk for a 10μg/m^(3)increase in PM_(10)showed similar estimates using PM concentrations{relative risks(RR)=1.007,95%confidence intervals(95%CI)=[1.002,1.011]}and EAC4 reanalysis data(RR=1.011,95%CI=[1.006,1.015]).However,the city-specific PM_(10)beta coefficients estimated using PM concentrations and EAC4 reanalysis data showed a low correlation(r=0.22).The use of reanalysis data should be approached with caution when assessing the association between particulate matter air pollution and health outcomes,particularly in cities with small populations.

Association between Ambient Air Pollution and Outpatient Visits for Acute Bronchitis in a Chinese City

Objective To investigate the short-term association between outdoor air pollution and outpatient visits for acute bronchitis,which is a rare subject of research in the mainland of China.Methods A time-series analysis was conducted to examine the association of outdoor air pollutants with hospital outpatient visits in Shanghai by using two-year daily data（2010-2011）.Results Outdoor air pollution was found to be associated with an increased risk of outpatient visits for acute bronchitis in Shanghai.The effect estimates of air pollutants varied with the lag structures of the concentrations of the pollutants.For lag06,a 10 μg/m3 increase in the concentrations of PM10,SO2,and NO2 corresponded to 0.94%（95% CI：0.83%,1.05%）,11.12%（95% CI：10.76%,11.48%）,and 4.84%（95% CI：4.49%,5.18%） increases in hospital visits for acute bronchitis,respectively.These associations appeared to be stronger in females（P〈0.05）.Between-age differences were significant for SO2（P〈0.05）,and between-season differences were also significant for SO2（P〈0.05）.Conclusion Our analyses have provided the first evidence that the current air pollution level in China has an effect on acute bronchitis and that the rationale for further limiting air pollution levels in Shanghai should be strengthened.

Short Term Impact of Air Pollution on Asthma Admission in Ulaanbaatar

Background: Asthma is a heterogeneous disease, usually characterized by chronic airway inflammation. The air quality is influenced by locations of the air pollution sources, their performance capacity, the technology used, the composition of waste generated and geographical and climate conditions. In this study, a time-series analysis was conducted to estimate the association of short-term exposure to ambient air pollutants and hospitalization due to asthma in Ulaanbaatar. Objectives: We estimate the short-term associations between daily changes in ambient air pollutants and daily asthma in Ulaanbaatar, Mongolia. Methods: This is a time-series cross over study. All asthma hospital admission and air pollution data of 2008-2017 was used for this assessment. Data analyzed by using the program STATA-12. For testing the differences of the results were used appropriate non-parametric tests. Result: The daily mean of sulfur dioxide concentration was 35.22 mg/m3 in the cold season, which was 7.57 times higher than the mean of the hot season. The mean annual PM 10 concentration was 182.73 μg/m3. Most of the cases of asthma were among women, aged between 5 - 64 years old, registered during winter and spring. 3.8 people admitted to the hospital mostly on weekdays. In all Lag of SO2, in Lag of NO2, in all Lag of PM 10, in PM 2.5 and in all Lag except for Lag 2 of CO, Lag 0 - 2 of O3 the incidence is likely to increase by 0.3% - 6.1% per 10 units of pollutants. Conclusion: The air pollution especially PM 10, PM 2.5, and CO are the most harmful air pollutants to asthma in Ulaanbaatar. The correlation mainly between asthma admission cases with meteorological parameters is because of the cold winter condition.

侧面爆破粉尘时空分布模拟分析

从开放性细颗粒物污染治理角度出发,建立建筑物拆除爆破产尘的几何计算模型,运用计算流体力学(CFD)工具,对给定环境风速下的建筑物周围压力场和速度场进行了模拟分析,并对侧面折叠爆破粉尘按R-R(Rosin-Rammler)分布,使用气流-粒子双向耦合的DPM模型研究了爆炸粉尘的时空分布。结果表明,下风侧因建筑物存在导致风速、压力急剧变化而产生涡流,涡流是影响爆破粉尘时空分布的关键因素之一。在时间上,爆破粉尘有气流拖曳运动状态和扩散态;在空间上,因重力作用致下部空间的粉尘浓度较大。

融合改进NBEATSx和时间注意力机制的空气污染预测

针对现有空气污染预测存在结构复杂、对多元变量与不同时间步间依赖关系提取不充分和多步预测精度低的问题,引入了β分布和非线性动态控制函数改进星鸦优化算法(INOA),优化NBEATSx模型参数,提高收敛精度;并融合时间模式注意力机制(TPA)为不同时间尺度的多外生变量自适应分配权重,再结合预测因子获取时间模式关系.利用所提模型对北京地区的PM2.5进行预测,与传统模型相比精度提高超过18.45%,为空气污染预测提供了一种新方法.

2016—2021年合肥市极端气温与循环系统疾病死亡的时间序列分析

目的探讨合肥市极端气温对居民循环系统疾病死亡的影响及不同人群的敏感性分析。方法收集合肥市2016—2021年逐日气象资料、大气污染物监测资料及循环系统疾病死亡数据。采用基于广义相加模型的分布滞后非线性模型(distributed lag non-linear model,DLNM),评估极端气温对不同性别、年龄人群循环系统疾病死亡影响以及对循环系统主要疾病死亡的滞后效应和累积效应。以日均气温中位数(17.7℃)为对照,计算极端气温的相对危险度(RR)。结果合肥市极端气温对居民循环系统疾病死亡具有显著影响。极端低温对循环系统疾病死亡影响滞后时间长,lag4时达到最大,RR(95%CI)为1.067(1.039,1.095),且不同人群的死亡风险均明显增加。极端高温对循环系统疾病死亡的影响在当天达到最大,RR(95%CI)为1.088(1.020,1.160),持续时间短;≥65岁、女性和脑血管病患者也均在当日效应最大,且效应具有统计学意义,而对其他人群无明显影响。极端气温对不同人群的冷效应均高于热效应,低温对<65岁人群的死亡风险明显高于≥65岁人群,热效应则相反;女性冷效应和热效应均高于男性;脑血管病人群冷效应和热效应也均高于缺血性心脏病人群。结论合肥市极端气温可能增加居民循环系统疾病死亡风险,冷效应影响更大,不同人群对冷热效应的敏感性有差别。

环渤海地区空气质量时空变化特征及动态预测

基于环渤海地区2017—2021年各城市空气质量指数(AQI)、污染物浓度与社会经济数据,利用数理统计、克里金插值法对环渤海地区AQI与污染物浓度的时空变化特征进行分析,运用皮尔逊相关性分析方法探讨AQI与污染物浓度、社会经济因素的相关关系,采用时间序列预测模型对2022年6月—2023年12月空气质量及污染物浓度进行预测。结果表明:环渤海地区AQI及污染物浓度大致呈逐年降低的趋势。AQI的逐月变化呈“W”形,O_(3)浓度的年内变化呈倒“V”形,其余污染物则呈现与O_(3)相反的变化趋势。AQI大致呈现西南高、东北低的空间分布特点,而污染物浓度分布具有明显的空间差异。环渤海地区5个代表性城市的AQI类别以良好为主,冬季首要污染物主要为PM_(2.5)、PM10,夏季首要污染物以O_(3)为主。人口数量是影响AQI的主要因素,城市园林绿地面积对AQI具有一定影响。预测结果显示,未来环渤海地区AQI、主要污染物浓度(O_(3)除外)均呈现出随时间的推移逐渐下降的变化趋势。

中国4个城市大气臭氧对呼吸系统疾病住院人数影响的时间序列分析

臭氧已成为影响中国空气质量最重要的污染物之一,为探究其对呼吸系统疾病住院人数的影响,收集保定、盐城、自贡和广州4个城市2019—2022年逐日因呼吸疾病住院人数、臭氧日最大8 h平均浓度(O_(3)-8h)和气象因素数据,采用广义相加模型在城市水平分析大气O_(3)-8h与呼吸系统疾病日住院人数的关联性,并采用随机效应Meta分析整合城市水平分析结果。结果表明:大气臭氧浓度升高可增加呼吸系统疾病日住院人数,并存在滞后效应;城市合并分析结果显示,O_(3)-8h每升高10μg·m^(-3),总呼吸系统疾病、慢性阻塞性肺病(COPD)和肺炎日住院人数分别增加0.49%(95%置信区间(CI):0.30%—0.68%,滞后3 d)、0.86%(95%CI:0.54%—1.18%,滞后2 d)和0.74%(95%CI:0.31%—1.17%,滞后4 d)。不同城市的效应强度以及最佳滞后时间存在较大差异;总体上,儿童和≥60岁老年人群对臭氧的呼吸系统效应更为敏感,不同性别间敏感性无显著差异。

大气污染与耳鼻咽喉急性疾病发病的时间序列分析

目的分析大气污染与耳鼻咽喉急性疾病发病的关系。方法回顾性收集2013至2018年杭州市主城区4家大型三级综合性医院(杭州市第一人民医院、浙江省中西医结合医院、杭州市中医院、杭州市第三人民医院)耳鼻咽喉急性疾病(包括急性扁桃体炎、急性会厌炎、急性喉炎、急性鼻-鼻窦炎、急性中耳炎)的门急诊量,并获取同时期该地区大气污染及气象参数数据。采用R统计软件建立广义相加模型,研究不同大气污染物浓度对耳鼻咽喉急性疾病门急诊量的影响,根据季节、年龄分层分析研究不同大气污染物浓度与门急诊量的关系。结果2013至2018年杭州市主城区耳鼻咽喉急性疾病门急诊总量333254人次,日均门急诊量为152人次。颗粒污染物(PM2.5、PM10)日均浓度每增加10μg/m^(3),在滞后第4天时对耳鼻咽喉急性疾病门急诊量的影响效应最大;气态污染物(SO2、NO2)日均浓度每增加10μg/m^(3),分别在滞后第4天、滞后第6天效应最大。单大气污染物模型中:年龄分层分析显示,在就诊的不同年龄亚组中,5~14岁年龄亚组中各大气污染物浓度与门急诊量的相关性最为明显,PM2.5、PM10、SO2、NO2浓度每升高10μg/m^(3),其效应排序为:NO2>SO2>PM10>PM2.5,RR值分别1.07(95%CI:1.05~1.09)、1.05(95%CI:1.03~1.07)、1.04(95%CI:1.02~1.05)、1.03(95%CI:1.02~1.05);季节分层分析显示,冷季(11月至4月)中大气污染物对耳鼻咽喉急性疾病门急诊量增加的影响强于暖季(5月至10月)(P<0.05)。在双大气污染物模型中,大气双污染物与耳鼻咽喉急性疾病门急诊量的相关性均有统计学意义(均P<0.05)。结论2013至2018年杭州市主城区大气污染物浓度升高与人群耳鼻咽喉急性疾病门急诊量增加相关,且具有滞后效应。耳鼻咽喉急性疾病的发病冷季效应强于暖季,5~14岁儿童的影响效应最为显著。这对制定耳鼻咽喉急性疾病的防治策略有重要参考价值。

2018—2021年成都市青羊区空气污染对呼吸系统疾病门诊量影响的时间序列分析

目的了解成都市青羊区空气污染物对呼吸系统疾病门诊量的影响。方法收集2018—2021年成都市青羊区空气污染物(PM_(2.5)、PM_(10)、NO_(2)、SO_(2)、CO、O3~8h)质量浓度和气象、医院呼吸系统疾病日门诊量资料。用Excel 2010和SPSS 25.0进行统计描述,在控制时间趋势、“星期几效应”“节假日效应”以及气象等混杂因素基础上,采用时间序列的广义相加模型,通过R 4.2.1对空气污染物和门诊量的关系进行时间序列分析。结果除O_(3~8 h)外各污染物质量浓度与呼吸系统疾病门诊量呈正相关。单污染物单日滞后模型显示:PM_(2.5)、PM_(10)、NO_(2)浓度每升高10μg/m^(3),暴露当日门诊就诊超额风险最高(分别为0.491%、0.508%、1.735%),SO_(2)在暴露滞后第1天门诊就诊超额风险最高(5.508%),CO在暴露滞后第7天对门诊量影响效应最大(0.052%)。单污染物平均滞后模型显示:PM_(2.5)在平均滞后1天效应值最大(0.520%),PM_(10)、NO_(2)、SO_(2)、CO在平均滞后7天效应值最大(分别为0.680%、4.241%、15.092%、0.147%)。结论空气污染物PM_(2.5)、PM_(10)、NO_(2)、SO_(2)、CO质量浓度升高,会使呼吸系统疾病门诊量增加,但最大效应值及其对应的滞后时间不同。

Prediction and Analysis of O_3 based on the ARIMA Model

The research conducted prediction on changes of atmosphere pollution during July 9, 2014-July 22, 2014 with SPSS based on monitored data of O3 in 13 successive weeks from 6 sites in Baoding City and demonstrated prediction effect of ARIMA model is good by Ljung-Box Q-test and R2, and the model can be used for prediction on future atmosphere pollutant changes.

杨凌区1990-2020年生态环境时空演变分析

基于GEE平台Landsat影像构建了杨凌区1990-2020年的遥感生态指数(RSEI),对生态环境进行监测评估,并结合Sen’s斜率、Mann-Kendall检验和Hurst指数进行时空过程分析。1990-2020年,RSEI均值从0.67降至0.55,先下降后不变,呈现西高东低的空间分布格局;生态质量变差面积从28.6%上升到60.6%,集中在城建区,变差重心东移,变好重心西移;生态环境质量变化趋势稳定,占68.59%,呈现持续性;社会经济发展对研究区生态环境质量存在负面影响,且人口指标的影响较大,得益于生态文明建设,2015年后生态环境状况得到稳定。杨凌区生态环境质量先退化后动态稳定,应控制裸地和建设用地面积,改善该地区生态环境水平。

基于后向气团轨迹的大气污染特征时序分析研究

大气污染特征时序分析方法主要通过计算不同区域内的污染颗粒平均浓度,锁定污染源的具体位置,由于缺乏对气团轨迹移动概率的分析,导致算法分析性能不佳。对此,提出基于后向气团轨迹的大气污染特征时序分析方法。预处理大气污染时间序列监测数据,结合皮尔逊相关系数法,计算时间序列数据之间的关联程度。网格划分研究区域,引入权重因子修正污染源轨迹,计算污染物权重浓度值,构建大气污染特征时序分析模型。测试结果表明,采用提出方法对大气污染时序数据分析时,污染源定位误差较低,具备较为理想的分析性能。

2015—2021年海口市大气污染物对肺癌发病影响的时间序列分析

目的探讨海口市大气污染物与肺癌发病的关联性及污染物对肺癌发病的滞后效应。方法收集2015—2021年海口市的肺癌日发病数、细颗粒物(fine particles,PM_(2.5))、可吸入颗粒物(inhalable particles,PM_(10))、NO_(2)、SO_(2)、CO、臭氧日最大8 h平均浓度(O3~8 h)和气象因素,采用基于泊松分布的广义相加模型建立单污染物和双污染物模型定量分析大气污染物与肺癌发病的关系。检验水准为α=0.05。结果2015—2021年海口市居民肺癌共2806例,肺癌日平均发生数为1.10例。在单污染物模型中,PM_(10)在滞后当天时,每升高10μg/m3引起肺癌日发病数的超额危险度(ER)最大,为6.33%(95%CI:2.29%~10.53%);SO_(2)在Lag6时效应值最大为41.82%(95%CI:3.19%~94.91%);NO_(2)在Lag6时效应值最大为18.43%(95%CI:7.97%~29.89%),CO在滞后当天效应值最大,即每升高1 mg/m3引起肺癌日发病数的ER为80.08%(95%CI:20.53%~169.06%)。在双污染物模型中,在引入PM_(10)后CO的效应值消失,引入PM_(2.5)、PM_(10)后SO_(2)的效应值消失,而分别引入其他污染物后,PM_(10)、SO_(2)、NO_(2)和CO对肺癌发病的影响效应有升有降,具有复杂的联合效应。结论2015—2021年海口市肺癌发病人数随PM_(10)、SO_(2)、NO_(2)和CO浓度的升高而上升,且存在滞后效应。

彭州市本地化环境空气质量健康指数(AQHI)构建

为了科学评估和系统表征大气污染对人群健康的潜在风险,研究通过采用广义相加时间序列模型(Generalized Additive Models,GAM),拟合了彭州市大气污染浓度与人群呼吸系统疾病就诊人数的暴露-反应关系,获取了相应的回归系数(β),构建了彭州市环境空气健康指数(Air Quality Health Index,AQHI)的计算方法并分级分类。结果表明,NO_(2)、PM_(10)、PM_(2.5)、O_(3)与人群呼吸系统疾病之间具有统计学意义上相关性,且最佳滞后时间及其相对危险度(Relative Risk,RR)存在差异,分别为0天(1.068)、7天(1.025)、7天(1.023)、1天(1.028),相应的回归系数(β)分别为0.0038872、0.0006119、0.0009031、0.0004203;构建的AQHI根据超额风险率(Extra-risk Rate,ER)共划分为4类11级,能够对大气污染潜在的健康风险进行评估,从而指导公众健康生活和出行,符合未来环境健康管理的趋势。

2022年广州市一次夏季臭氧污染过程成因分析

为探究持续减排背景下广州市夏季臭氧(O_(3))污染成因,选取2022年夏季首次O_(3)污染过程进行研究。文章结合O_(3)及其前体物观测数据、气象观测数据,从气象条件、区域传输和O_(3)前体物排放等方面较为全面地分析了此次污染过程的特征及传输对O_(3)的影响。结果表明,观测期间受副高控制,低湿高温有利于O_(3)生成,小风时数增多,污染扩散条件差,日照时数加长,有利于光化学反应。污染天主导风向为南风,本轮污染过程由南向北推进,O_(3)小时变化出现双峰拖尾峰现象,存在一定区域污染和传输影响。污染前一晚及当日凌晨,NO_(2)浓度和挥发性有机物均偏高,为O_(3)生成提供了充分条件。