室内空气和灰尘中全(多)氟烷基化合物的研究进展

全(多)氟烷基化合物(per(poly)fluoroalkyl substances,PFASs)在环境各个介质及人体样品中广泛被检出,近年,在室内空气和灰尘中也普遍发现PFASs.研究表明,室内空气中PFASs的含量普遍高于室外空气,室内空气和灰尘中的PFASs可能是室外空气的污染来源及人体暴露源,因此室内环境中PFASs成为环境领域的又一个研究热点.但目前为止,我国还没有开展室内空气中PFASs的相关研究,室内灰尘中PFASs的研究也相对较少.本文就室内空气和灰尘中PFASs的采样与分析方法、污染现状、来源分析及人体暴露等4个方面进行了综合阐述,以期为我国室内环境中PFASs的研究提供参考.     

Sources, distribution, and risk assessment of organochlorine pesticides in Nairobi City, Kenya

The distribution and sources of organochlorine pesticides (OCPs) in air and surface waters were monitored in Nairobi City using triolein-filled semipermeable membrane devices (SPMDs). The SPMDs were extracted by dialysis using n-hexane, followed by cleanup by adsorption chromatography on silica gel cartridges. Sample analysis was done by GC-ECD and confirmed by GC–MS. Separation of means was achieved by analysis of variance, followed by pair-wise comparison using the t-test (p≤ 0.05). The total OCPs ranged between 0.018 – 1.277 ng/m³ in the air and <LOD – 1391.000 ng/m³ in surface waters. Based on the results, the means of Industrial Area, Dandora and Kibera were not significantly different (p≤ 0.05), but were higher (p≤ 0.05) than those of City square and Ngong’ Forest. The results revealed non-significant (p≤ 0.05) contribution of long-range transport to OCP pollution in Nairobi City. This indicated possible presence of point sources of environmental OCPs in the city. The water-air fugacity ratios indicated that volatilization and deposition played an important role in the spatial distribution of OCPs in Nairobi City. This indicated that contaminated surface waters could be major sources of human exposure to OCPs, through volatilization. The incremental lifetime cancer risks (ILCR) determined from inhalation of atmospheric OCPs were 2.3745  ×  10^?13 – 1.6845  ×  10^?11 (adult) and 5.5404  ×  10^?13 – 3.9306  ×  10^?11 (child) in the order: Dandora > Kibera > Industrial Area > City Square > Ngong’ Forest. However, these were lower than the USEPA acceptable risks, 10^?6 – 10^?4. This study concluded that atmospheric OCPs did not pose significant cancer risks to the residents.     

Particulate matter exchange between atmosphere and roads surfaces in urban areas

The deposition and the re-suspension of particulate matter (PM) in urban areas are the key processes that contribute not only to stormwater pollution, but also to air pollution. However, investigation of the deposition and the re-suspension of PM is challenging because of the difficulties in distinguishing between the resuspended and the deposited PM. This study created two Bayesian Networks (BN) models to explore the deposition and the re-suspension of PM as well as the important influential factors. The outcomes of BN modelling revealed that deposition and re-suspension of PM10 occurred under both, high-traffic and low-traffic conditions, and the re-suspension of PM2.5 occurred under low-traffic conditions. The deposition of PM10 under low-volume traffic condition is 1.6 times higher than under high-volume traffic condition, which is attributed to the decrease in PM10 caused by relatively higher turbulence under high-volume traffic conditions. PM10 is more easily resuspended from road surfaces compared to PM2.5 as the particles which larger than the thickness of the laminar airflow over the road surface are more easily removed from road surfaces. The increase in wind speed contributes to the increase in PM build-up by transporting particulates from roadside areas to the road surfaces and the airborne PM2.5 and PM10 increases with the increase in relative humidity. The study outcomes provide a step improvement in the understanding of the transfer processes of PM2.5 and PM10 between atmosphere and urban road surfaces, which in turn will contribute to the effective design of mitigation measures for urban stormwater and air pollution.     

Human viral pathogens are pervasive in wastewater treatment center aerosols

Wastewater treatment center(WTC) workers may be vulnerable to diseases caused by viruses, such as the common cold, influenza and gastro-intestinal infections. Although there is a substantial body of literature characterizing the microbial community found in wastewater, only a few studies have characterized the viral component of WTC aerosols,despite the fact that most diseases affecting WTC workers are of viral origin and that some of these viruses are transmitted through the air. In this study, we evaluated in four WTCs the presence of 11 viral pathogens of particular concern in this milieu and used a metagenomic approach to characterize the total viral community in the air of one of those WTCs. The presence of viruses in aerosols in different locations of individual WTCs was evaluated and the results obtained with four commonly used air samplers were compared.We detected four of the eleven viruses tested, including human adenovirus(h Ad V),rotavirus, hepatitis A virus(HAV) and Herpes Simplex virus type 1(HSV1). The results of the metagenomic assay uncovered very few viral RNA sequences in WTC aerosols, however sequences from human DNA viruses were in much greater relative abundance.     

A Variance Estimator for Constrained Estimates of Change in Relative Categorical Frequencies

Consistent estimators of change and state becomes an issue when sample data come from a mix of permanent and temporary observation units. A joint maximum likelihood estimator of state and change creates estimates of state that depend on antecedent viz. posterior survey results and may differ from estimates of state derived from a single-date analysis of the sample data. A constrained estimator of change in relative categorical frequencies that eliminates this potential inconsistency is proposed and a model based estimator of their sampling variance is developed. The performance of the constrained estimator is quantified against six criteria and a joint maximum likelihood estimator in simulated sampling from 15 populations with three combinations of permanent and temporary samples, four to six categorical class attributes, and constant size between sampling dates. Bias of the constrained estimators was negligible but larger than for joint maximum likelihood estimators. Mean absolute deviations and variances of constrained estimators were generally at par with the joint estimators. Constrained estimators of root mean square errors and achieved coverage of nominal confidence intervals of constrained estimators were occasionally better. A generalized variance function for the constrained estimates of change is provided as a computational shortcut.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

连续采样与五日法采样效果及费用的对比分析

通过对连续采样与五日法采样五种方法监测结果及所需费用的比较，得出连续采样方法监测结果更具有代表性，合理性，一次性投资大，但运行费用并不高的结论。     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.