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.     

The organic composition of diesel particulate matter, diesel fuel and engine oil of a non-road diesel generator

Diesel-powered equipment is known to emit significant quantities of fine particulate matter to the atmosphere. Numerous organic compounds can be adsorbed onto the surfaces of these inhalable particles, among which polycyclic aromatic hydrocarbons (PAHs) are considered potential occupational carcinogens. Guidelines have been established by various agencies regarding diesel emissions and various control technologies are under development. The purpose of this study is to identify, quantify and compare the organic compounds in diesel particulate matter (DPM) with the diesel fuel and engine oil used in a non-road diesel generator. Approximately 90 organic compounds were quantified (with molecular weight ranging from 120 to 350), which include alkanes, PAHs, alkylated PAHs, alkylbenzenes and alkanoic acids. The low sulfur diesel fuel contains 61% alkanes and 7.1% of PAHs. The identifiable portion of the engine oil contains mainly the alkanoic and benzoic acids. The composition of DPM suggests that they may be originated from unburned diesel fuel, engine oil evaporation and combustion generated products. Compared with diesel fuel, DPM contains fewer fractions of alkanes and more PAH compounds, with the shift toward higher molecular weight ones. The enrichment of compounds with higher molecular weight in DPM may be combustion related (pyrogenic).     

工作场所空气中硫酸雾（SO3）的分析

采用碘量法测定工作场所空气中的三氧化硫及硫酸雾，三氧化硫及硫酸雾经中性水吸收后与碘化钾和碘酸钾反应，析出的碘与淀粉指示剂作用产生蓝色，进行比色测定。     

地面水环境质量评价的有时序多目标决策方法

将有时序多目标决策方法应用于地面水环境质量优势的比较，介绍了决策方法的原理和评价步骤。实例分析表明该方法计算过程简便，分辨率高，排序结果与实际环境质量状况相符合。     

发光细菌法评价工业废水的毒性

用发光细菌测定法估算了两城市污水对河道水体污染的分担率，评价了工厂排放废水的毒性，并据此确定了污染源监督管理中应优先治理的工厂（车间）废水。     

地下水环境质量变化动态研究

以邢台市环境保护监测站１０年来的监测资料为依据，运用单因子比值法，环比发展速率外推法和直线回归法，对地下水环境质量现状及１０年的动态变化进行了分析，研究和评述，并对发展趋势做了预测性评价。     

Methane emissions from natural wetlands

Methane is considered one of the most important greenhouse gases in the atmosphere. Because of the strict anaerobic conditions required by CH₄-generating microorganisms, natural wetland ecosystems are one of the main sources of biogenic CH₄. The total natural wetland area is estimated to be 5.3 to 5.7 × 10¹² m², making up less than 5% of the Earth's land surface. However, natural wetland plays a disproportionately large role in CH₄ emissions. Wetlands are likely the largest natural sources of CH₄ to the atmosphere, accounting for about 20% of the current global annual emission. Out of the total amount of CH₄ emitted, northern wetlands contribute 34%, temperate wetlands 5%, and tropical systems about 60%.Because of the unique characteristics and high productivity, wetland ecosystems are important in the global carbon cycle. Natural wetlands are permanently or temporarily saturated. Strict anaerobic conditions consequently develop, which allows methanogenesis to occur. But the thin oxic layer and the oxic plant rhizophere promote activity of CH₄-oxidizing bacteria or methanotrophs. Thus, both CH₄ formation and consumption in wetland systems are microbiological processes and are controlled by many factors. Eight of the controlling factors, including carbon supply, soil oxidation-reduction status, pH, temperature, vegetation, salinity and sulfate content, soil hydrological conditions and CH₄ oxidation are discussed in this paper.     

用密切值法评价沙湾荒漠区生态环境质量

利用多目标决策中的密切值法进行沙湾荒漠区生态环境综合评价。该方法简便、适用、且分辨率较高，对各监测点群的定量描述符合实际状况。     

“引江济太”对太湖北部底质及水质总磷的影响

应用GIS技术研究了"引江济太"对太湖北部底质与水质总磷(TP)浓度的影响。研究结果表明,2014—2019年,调水北部沿线4个点位底质TP浓度降幅为59.8%～80.5%;不同点位的水质TP浓度变化存在差异,入湖口点位下降14.1%,沿线其他点位分别上升37.4%、38.6%和45.0%。从空间分布来看,太湖水质TP始终呈明显的"西高东低"分布特点,但底质TP的分布未显示出该类规律。望亭水利枢纽调水情况与入湖口5#点位底质TP浓度呈一定程度的正相关,在调水入湖水量大于出湖水量的年份,5#点位底质TP浓度会偏高。"引江济太"会对太湖北部调水沿线,特别是入湖口附近水域的TP含量产生较大影响,并通过水流迁移与底质再悬浮释放影响下游水域。如何降低望虞河及周边支流的入湖泥沙量将是今后开展科学调水、保障贡湖水源地水质安全的重点研究方向之一。     

2017年上半年北京市及周边地区空气质量特征分析

依托北京市、廊坊市和保定市高密度的地面空气质量监测、气象要素监测以及PM_2.5化学组分监测和后向轨迹分析等手段，对2017年上半年三地的空气质量进行分析。研究发现：三地中北京市空气质量较好，保定市较差。分污染物来看，保定市SO₂浓度水平明显高于廊坊市和北京市，颗粒物PM₁₀和PM_2.5也呈现保定市最高、北京市最低的规律。从污染物日变化来看，CO、SO₂、NO₂、PM₁₀和PM_2.5呈双峰型分布，O₃呈单峰型分布。从区域整体分布规律来看，PM_2.5和SO₂呈现明显的"南高北低"特征。PM_2.5化学组分分析结果表明：1—4月燃煤对该区域空气质量的影响较大，5—6月机动车排放的影响更为凸显。后向轨迹分析结果表明：在2017年上半年到达北京市的气流中有24%来自于北京市南部，且这些气流多为低空传输，表明区域传输对于北京市空气质量具有一定的影响。