Analysis and prediction of $$\mathrm{PM}_{10}$$ concentration levels in Tunisia using statistical learning approaches

Over the past years, the health impact of airborne particulate matter \(\mathrm{PM}_{10}\) has become a very topical subject. Thereby, a lot of research effort in the environmental sciences goes towards the modeling and the prediction of ambient \(\mathrm{PM}_{10}\) concentrations. In this paper, we are interested in the statistical classification of the daily mean \(\mathrm{PM}_{10}\) concentration in Tunisia according to the authority regulation. We consider two monitoring stations: a big industrial station and a traffic station. The main goal of this work is to determine the pertinent predictors of \(\mathrm{PM}_{10}\) concentration within a nonlinear multiclass framework. To do this, we used two popular statistical learning methods; the support vector machines (SVM) and the random forests (RF). The statistical results obtained on the real datasets, show that RF outperform SVM for the purpose of variable selection even with a reduced number of observations compared to the number of explicative variables. It was also demonstrated that the \(\mathrm{PM}_{10}\) concentration measured yesterday is the most relevant predictor of its present-day value. Moreover, we found that the more delayed values of \(\mathrm{PM}_{10}\) concentration may be crucial to get an accurate prediction.     

Vertical oil dispersion profile under non-breaking regular waves

An experimental program was conducted to investigate vertical oil dispersion of surface oil spills under non-breaking regular waves. The variation in oil concentration caused by oil dispersion in a water column was studied to determine the vertical oil dispersion profile. The experiments were performed using different waves characteristics for different volumes of oil spill to evaluate the variation in oil concentration at three depths at two sampling stations. The correlations between oil concentration and the main parameters of wave characteristics, oil spill volume, sampling depth, and distance of sampling stations to spill location were assessed. The results revealed that the trend of variation in oil concentration versus wave steepness is linear. The results obtained from experimental measurements indicated that the oil concentrations at mid-depth were 44–77 % and the concentrations near the flume bed were 12–33 % of the concentration near the water surface.     

Prediction of mass and momentum transport in turbulent plane wall jets over smooth and transitionally rough surfaces

This paper is concerned with the prediction of mass and momentum transport in turbulent wall jets developing over smooth and transitionally rough plane walls. The ability to accurately predict the resulting wall shear stresses and vertical profiles of the Reynolds stresses in these flows is prerequisite to the accurate prediction of bed scour, sediment re-suspension and transport by turbulent diffusion. The computations were performed by solving the Reynolds-averaged forms of the equations describing conservation of mass, momentum and concentration. The unknown correlations that arise from the averaging process (the Reynolds stresses in the case of the momentum equation, and the turbulent mass fluxes in the case of concentration) were obtained from the solution of modeled differential equations that describe their conservation. Since these models are somewhat more complex than those typically used in practice, their benefits are demonstrated by comparisons with results obtained from simpler, eddy-viscosity based closures. Comparisons with experimental data show that results of acceptable accuracy can be obtained only by using the appropriate combination of models for the turbulent fluxes of mass and momentum that properly account for the reduction of the Reynolds stresses due to wall damping effects, and for the modification of the mass transfer rates due to interactions with the mean rates of strain.     

Simulations of the flow in the Mahakam river–lake–delta system,Indonesia

Large rivers often present a river–lake–delta system, with a wide range of temporal and spatial scales of the flow due to the combined effects of human activities and various natural factors, e.g., river discharge, tides, climatic variability, droughts, floods. Numerical models that allow for simulating the flow in these river–lake–delta systems are essential to study them and predict their evolution under the impact of various forcings. This is because they provide information that cannot be easily measured with sufficient temporal and spatial detail. In this study, we combine one-dimensional sectional-averaged (1D) and two-dimensional depth-averaged (2D) models, in the framework of the finite element model SLIM, to simulate the flow in the Mahakam river–lake–delta system (Indonesia). The 1D model representing the Mahakam River and four tributaries is coupled to the 2D unstructured mesh model implemented on the Mahakam Delta, the adjacent Makassar Strait, and three lakes in the central part of the river catchment. Using observations of water elevation at five stations, the bottom friction for river and tributaries, lakes, delta, and adjacent coastal zone is calibrated. Next, the model is validated using another period of observations of water elevation, flow velocity, and water discharge at various stations. Several criteria are implemented to assess the quality of the simulations, and a good agreement between simulations and observations is achieved in both calibration and validation stages. Different aspects of the flow, i.e., the division of water at two bifurcations in the delta, the effects of the lakes on the flow in the lower part of the system, the area of tidal propagation, are also quantified and discussed.     

Photochemical indicators of ozone sensitivity: application in the Pearl River Delta,China

Surface O₃ production has a highly nonlinear relationship with its precursors. The spatial and temporal heterogeneity of O₃-NO _x -VOC-sensitivity regimes complicates the control-decision making. In this paper, the indicator method was used to establish the relationship between O₃ sensitivity and assessment indicators. Six popular ratios indicating ozone-precursor sensitivity, HCHO/NO _y , H₂O₂/ HNO₃, O₃/NO _y , O₃/NO _z , O₃/HNO₃, and H₂O₂/NO _z , were evaluated based on the distribution of NO_x- and VOC-sensitive regimes. WRF-Chem was used to study a serious ozone episode in fall over the Pearl River Delta (PRD). It was found that the south-west of the PRD is characterized by a VOCsensitive regime, while its north-east is NO _x -sensitive, with a sharp transition area between the two regimes. All indicators produced good representations of the elevated ozone hours in the episode on 6 November 2009, with H₂O₂/HNO₃ being the best indicator. The threshold sensitivity levels for HCHO/NO_y, H₂O₂/HNO₃, O₃/NO _y , O₃/NO _z , O₃/HNO₃, and H₂O₂/NO _z were estimated to be 0.41, 0.55, 10.2, 14.0, 19.1, and 0.38, respectively. Threshold intervals for the indicators H₂O₂/HNO₃, O₃/NO _y , O₃/NO _z , O₃/HNO₃, and H₂O₂/NO _z were able to identify more than 95% of VOC- and NO _x -sensitive grids. The ozone episode on 16 November 16 2008 was used to independently verify the results, and it was found that only H₂O₂/HNO₃ and H₂O₂/NO _z were able to differentiate the ozone sensitivity regime well. Hence, these two ratios are suggested as the most appropriate indicators for identifying fall ozone sensitivity in the PRD. Since the species used for indicators have seasonal variation, the utility of those indicators for other seasons should be investigated in the future work.

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Improving simulations of sulfate aerosols during winter haze over Northern China: the impacts of heterogeneous oxidation by NO<Subscript>2</Subscript>

We implemented the online coupled WRF-Chem model to reproduce the 2013 January haze event in North China, and evaluated simulated meteorological and chemical fields using multiple observations. The comparisons suggest that temperature and relative humidity (RH) were simulated well (mean biases are–0.2K and 2.7%, respectively), but wind speeds were overestimated (mean bias is 0.5 m?s^–1). At the Beijing station, sulfur dioxide (SO₂) concentrations were overpredicted and sulfate concentrations were largely underpredicted, which may result from uncertainties in SO₂ emissions and missing heterogeneous oxidation in current model. We conducted three parallel experiments to examine the impacts of doubling SO₂ emissions and incorporating heterogeneous oxidation of dissolved SO₂ by nitrogen dioxide (NO₂) on sulfate formation during winter haze. The results suggest that doubling SO₂ emissions do not significantly affect sulfate concentrations, but adding heterogeneous oxidation of dissolved SO₂ by NO₂ substantially improve simulations of sulfate and other inorganic aerosols. Although the enhanced SO₂ to sulfate conversion in the HetS (heterogeneous oxidation by NO₂) case reduces SO₂ concentrations, it is still largely overestimated by the model, indicating the overestimations of SO₂ concentrations in the North China Plain (NCP) are mostly due to errors in SO₂ emission inventory.

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Approaching the binding between Cu(II) and aerobic granules by a modified titration and µ-XRF

Interactions between metals and activated sludge can substantially affect the fate and transport of heavy metals in wastewater treatment plants. Therefore, it is important to develop a simple, fast and efficient method to elucidate the interaction. In this study, a modified titration method with a dynamic mode was developed to investigate the binding of Cu(II), a typical heavy metal, onto aerobic granules. The titration results indicated that pH and ionic strength both had a positive effect on the biosorption capacity of the granular sludge. The µ-XRF results demonstrated that the distribution of metals on the granular surface was heterogeneous, and Cu showed strong correlations and had the same “hot spots” positions with other metal ions (e.g., Ca, Mg, Fe etc.). Ion exchange and complexing were the main mechanisms for the biosorption of Cu(II) by aerobic granules. These results would be beneficial for better understanding of Cu(II) migration and its fate in wastewater treatment plants.     

我国全氟辛烷磺酸盐分子毒理学研究进展：纪念金一和教授

全氟化合物(PFCs)在工业和生活中应用广泛,但是PFCs对生物体存在潜在的毒性,其中以8碳链的PFCs,如全氟辛烷磺酸(PFOS)和全氟辛烷羧酸(PFOA)应用最多。这类化合物在环境中很难降解,并能在生物体内富集^[1-6]。2009年PFOS被列入新增POPs名单,PFOS的毒性毒理研究随即成为研究热点。目前,国内研究者已陆续展开
PFOS对各种动物^[7-17],如昆虫、鸟类、鱼类、贝类、两栖类、哺乳类和人体细胞的急慢性毒性。但是这些关于PFOS毒性的研究所用剂量较高,远离了环境浓度范围,而高剂量的毒性效应与低剂量的毒性效应存在很大差异,尤其体现在不同剂量下作用的靶器官和信号通路的生物分子学响应会有很大差异。PFCs除了对肝脏造成慢性损伤、对免疫系统和生殖内分泌系统有明显影响外,还具有神经毒性。
已有的研究结果表明PFCs神经毒性潜在的机制主要有：影响神经元的生长分化、突触发生和脑发育;影响神经递质如多巴胺、谷氨酸和乙酰胆碱的水平;通过ROS诱导神经细胞凋亡;影响信号转导途径;影响甲状腺系统。在PFOS对神经系统毒性毒理方面,金一和教授课题组的研究工作十分系统。
金一和教授课题组针对PFOS对神经系统的影响,由浅入深地展开系列研究,提出了神经系统可能是PFOS的靶系统之一^[18-23]。针对外源性化合物易于蓄积和产生毒害的靶器官——海马结构,他们通过中枢神经系统兴奋性氨基酸和谷氨酰胺合成酶的剂量相应关系分析,提出了氨基酸类神经递质含量的改变是PFOS神经毒性的效应标记之一^[18-20]。这些研究在PFOS是如何干扰氨基酸类神经递质代谢,导致神经细胞损伤及神经功能损害方面给出了重要的线索。同时,他们通过对神经系统发育影响研究和基因组学手段,得出了PFOS会对后代学习记忆功能和脑的健康发育产生不良影响,并提出PFOS可能通过改变脑组织血氧平衡,影响中枢神经系统功能和发育过程的毒理学作用机制^[23-26]。
在PFOS对器官损伤机理方面,金一和教授课题组也做出了许多工作^[27-32]：提出PFOS的主要靶系统中还包括免疫系统,并用来解释PFOS染毒后,各脏器器官所呈现的功能下降的现象。
遗憾的是,金一和教授关于PFCs与生命系统相互作用机理的研究正在蒸蒸日上,他就离所热爱的事业而去。我们也只能以此文给金一和教授一生中最精彩的部分工作一个不完善的总结。金一和教授系统深入的研究成果为我国PFCs的分子毒理学研究奠定了坚实的基础,他颇具前瞻性的研究思想为后继者提供了启迪科研灵感的源泉。
     

Substance flow analysis for an urban drainage system of a representative hypothetical city in China

This paper discusses the use of substance flow analysis (SFA) as a tool to support quantified research on urban drainage systems. Based on the principle of mass balance, a static substance flow model is established to describe and examine the routes and intensities of water, chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) for a representative hypothetical city (RH city) in China, which is a devised and scaled city using statistical characteristics of urban circumstances at the national level. It is estimated that the annual flux of water, COD, TN and TP through the urban drainage system in 2010 was 55.1 million m³, 16037.3 t, 1649.5 t and 209.7 t, respectively. The effluent of wastewater treatment plant (WWTP) was identified as the most important pathway for pollutant emissions, which contributed approximately 60% of COD, 65% of TN and 50% of TP to receiving water. During the wastewater treatment process, 1.0 million m3, 7042.5 t, 584.2 t and 161.4 t of the four studied substances had been transmitted into sludge, meanwhile 3813.0 t of COD and 394.0 t of TN were converted and emitted to the atmosphere. Compared with the representative hypothetical city of 2000, urban population and the area of urban built districts had expanded by approximately 90% and 80% respectively during the decade, resulting in a more than threefold increase in the input of substances into the urban drainage system. Thanks to the development of urban drainage systems, the total loads of the city were maintained at a similar level.     

Platinum pollution in road dusts,roadside soils,and tree barks in Seoul,Korea

This study presents the level of platinum in urban environment in and around Seoul, the capital city of Korea. Road dust, roadside soil, and tree bark samples were collected from the sites of various traffic volumes and from control sites in the suburbs. The above samples were analyzed for Pt by ICP-MS and other heavy metals by ICP-OES. Platinum levels in road dusts and roadside soils from Seoul were in the range of 3.8–444 ng/g (av. 115.0 ng/g) and 0.7–221 ng/g (av. 49.7 ng/g), respectively, whereas those in the suburbs were in the range of 2.3–5.2 ng/g (av. 3.9 ng/g) in road dusts and 0.4–5.1 ng/g (av. 2.4 ng/g) in roadside soils. The highest Pt levels in road dusts were found from major roads with high traffic volume. The remarkable difference in average Pt level between heavy traffic roads (av. 132.2 ng/g) and light traffic roads (av. 22.8 ng/g) reflects that an important source of Pt in roadside environment is automobile catalytic converter. High Pt level in road dust was found from the site of erratic stop–start driving condition, for example, 178 ng/g Pt in road dust around a vehicle crossing gate. Platinum level in tree barks ranged from 0.9 to 4.5 ng/g, which indicates the existence of Pt-containing particulate matter in the atmosphere. Road dusts with high Pt level were enriched in traffic-related heavy metals.