A multivariate examination of ‘artificial mussels’ in conjunction with spot water tests in freshwater ecosystems

Metal pollution in aquatic systems is considered a serious environmental issue globally due to their ability to accumulate in aquatic environments. Wetlands are vulnerable to this pollution as they are known to trap toxins, removing them from the water. Artificial mussel technology, originally developed for marine environments, was applied to this freshwater system and spot water samples were collected. The Nyl River floodplain (Ramsar classified) is one of the largest and most ecologically significant wetlands in South Africa. The aims of this study were to determine metal contamination along the Nyl River system by means of artificial mussels (AM) and water ICP-MS analysis and to determine whether the use of AMs in conjunction with spot water testing could give more insight into the pollution in freshwater wetlands. The concentrations of Al, Cd, Cr, Co, Cu, Fe, Mn, Ni, Pb and Zn were determined at seven sites. It was determined that the levels accumulated by the AMs differed in spatial and temporal trends when compared to spot water samples. It was determined that there were high levels of some metals found in the spot water tests that were not corroborated by the AMs results, which could indicate isolated pollution events. The use of AMs in conjunction with spot water testing was determined to be beneficial in gaining deeper insight into water metal conditions in dynamic freshwater systems.     

Multivariate analyses of the effect of an urban wastewater treatment plant on spatial and temporal variation of water quality and nutrient distribution of a tropical mid-order river

Freshwater resources are increasingly scarce due to human activities, and the understanding of water quality variations at different spatial and temporal scales is necessary for adequate management. Here, we analyze the hypotheses that (1) the presence of a wastewater treatment plant (WWTP) and (2) a polluted tributary that drains downstream from the WWTP change the spatial patterns of physicochemical variables (pH, turbidity, dissolved oxygen, and electrical conductivity) and nutrient concentrations (reactive soluble phosphorus, total phosphorus, nitrogen series, total nitrogen, and total dissolved carbon) along a mid-order river in SE Brazil and that these effects depend on rainfall regime. Six study sites were sampled along almost 4 years to evaluate the impacts of human activities, including sites upstream (1–3) and downstream (5–6) from the WWTP. The impacts were observed presenting an increasing trend from the source (site 1) towards Água Quente stream (site 4, the polluted tributary), with signs of attenuation at site 5 (downstream from both WWTP and site 4) and the river mouth (site 6). Input of nutrients by rural and urban runoff was observed mainly at sites 2 and 3, respectively. At sites 4 and 5, the inputs of both untreated and treated wastewaters increased nutrient concentrations and changed physicochemical variables, with significant impacts to Monjolinho River. Seasonal variations in the measured values were also observed, in agreement with the pluviometric indexes of the region. Univariate analyses suggested no effect of the WWTP for most variables, with continued impacts at sites downstream, but non-parametric multivariate analysis indicated that these sites were recovering to chemical characteristics similar to upstream sites, apparently due to autodepuration. Therefore, multivariate methods that allow rigorous tests of multifactor hypotheses can greatly contribute to determine effects of both point and non-point sources in river systems, thus contributing to freshwater monitoring and management.     

An automated (novel) algorithm for estimating green vegetation cover fraction from digital image: UIP-MGMEP

Green vegetation cover fraction (VCF) is an important indicator of vegetation status in ecology and agronomy. Digital image analysis (DIA) has been widely accepted as a new VCF measurement technique. In this study, we present a novel fully automatic threshold segmentation algorithm for VCF measurements, which is named as upper inflection point plus mean gradient magnitude of edge pixels (UIP-MGMEP). The algorithm performs VCF estimation upon the vegetation index Excess Green (EXG). UIP-MGMEP optimizes the EXG threshold by searching the upper inflection point (UIP) of the M-Et curve (mean gradient magnitude of edge pixels (MGMEP) vs. EXG threshold), based on the assumption that EXG variance of the boundary pixels between vegetation and background is larger than the variance of the background. Five typical sample images are used to illustrate how ground complexity reduces the distinctness of the UIP. Three controlled experiments are illustrated to test the robustness of UIP-MGMEP to resolution, exposure, and ground complexity. The results show that UIP-MGMEP is a promising algorithm for automatic VCF estimation upon digital images. Compared to broad-leaved grass, narrow-leaved grass is more sensitive to resolution and exposure. To reduce ground complexity, smaller footprint size while more images to cover the same area may be better than one image with large footprint size. UIP-MGMEP is fully automatic, making it promising for batch processing of VCF measurements that is very difficult in any wide-range field survey in the past. UIP-MGMEP algorithm can only extract green vegetation and is not suitable for non-green (even grayish-green) vegetation, due to the limits of vegetation index EXG. In addition, UIP-MGMEP is not recommended for images with VCF less than 0.5% or greater than 99.5%.     

Urban tracer dispersion experiments during the second DAPPLE field campaign in London 2004

As part of the DAPPLE programme two large scale urban tracer experiments using multiple simultaneous releases of cyclic perfluoroalkanes from fixed location point sources was performed. The receptor concentrations along with relevant meteorological parameters measured are compared with a three screening dispersion models in order to best predict the decay of pollution sources with respect to distance. It is shown here that the simple dispersion models tested here can provide a reasonable upper bound estimate of the maximum concentrations measured with an empirical model derived from field observations and wind tunnel studies providing the best estimate. An indoor receptor was also used to assess indoor concentrations and their pertinence to commonly used evacuation procedures.     

Comprehensive analysis of the carbon impacts of vehicle intelligent speed control

In recent years sophisticated technologies have been developed to control vehicle speed based on the type of road the vehicle is driven on using Global Positioning Systems and in-car technology that can alter the speed of the vehicle. While reducing the speed of road vehicles is primarily of interest from a safety perspective, vehicle speed is also an important determinant of vehicle emissions and thus these technologies can be expected to have impacts on a range of exhaust emissions. This work analyses the results from a very large, comprehensive field trial that used 20 instrumented vehicles with and without speed control driven almost 500,000 km measuring vehicle speed at 10 Hz. We develop individual vehicle modal emissions models for CO₂ for 30 Euro III and Euro IV cars at a 1-Hz time resolution. Generalized Additive Models were used to describe how emissions from individual vehicles vary depending on their driving conditions, taking account of variable interactions and time-lag effects. We quantify the impact that vehicle speed control has on-vehicle emissions of CO₂ by road type, fuel type and driver behaviour. Savings in CO₂ of ≈6% were found on average for motorway-type roads when mandatory speed control was used compared with base case conditions. For most other types of road, speed control has very little effect on emissions of CO₂ and in some cases can result in increased emissions for low-speed limit urban roads. We also find that there is on average a 20% difference in CO₂ emission between the lowest and highest emitting driver, which highlights the importance of driver behaviour in general as a means of reducing emissions of CO₂.     

Influence of uncertain reaction rates on ozone sensitivity to emissions

Air quality models rely upon simplified photochemical mechanisms to efficiently represent the thousands of chemical species that interact to form air pollution. Uncertainties in the chemical reaction rate constants and photolysis frequencies that comprise those mechanisms can generate uncertainty in the estimation of pollutant concentrations and their responsiveness to emission controls. A high-order sensitivity analysis technique is applied to quantify the extent to which reaction rate uncertainties influence estimates of ozone concentrations and their sensitivities to precursor emissions during an air pollution episode in Houston, Texas. Several reactions were found to have much larger proportional effects on ozone’s sensitivities to emissions than on its concentrations. In particular, uncertainties in photolysis frequencies and in the rate of reaction between NO₂ and OH to form nitric acid can significantly influence the magnitude and sign of peak ozone sensitivity to nitrogen oxide (NO_x) emissions. Ozone sensitivity to VOCs exhibits a much more muted response to uncertainties in the reaction rate constants and photolysis frequencies considered here. The results indicate the importance of accurate reaction rate constants to predicting the ozone impacts resulting from NO_x emission controls.     

Linear and non-linear relationships mapping the Henry’s law parameters of organic pesticides

This work aims to predict the air to water partitioning for 96 organic pesticides by means of the Quantitative Structure–Property Relationships Theory. After performing structural feature selection with Genetics Algorithms and Replacement Method linear approaches, it is found that among the most important molecular features appears the Moriguchi octanol–water partition coefficient, and higher lipophilicities would lead to compounds having higher Henry’s law constants. We also compare the statistical performance achieved by four fully-connected Feed-Forward Multilayer Perceptrons Artificial Neural Networks. The statistical results found reveal that the best performing model uses the Levenberg–Marquardt with Bayesian regularization (BR) weighting function for achieving the most accurate predictions.     

Economic damages of ozone air pollution to crops using combined air quality and GIS modelling

This study aims at presenting a combined air quality and GIS modelling methodological approach in order to estimate crop damages from photochemical air pollution, depict their spatial resolution and assess the order of magnitude regarding the corresponding economic damages. The analysis is conducted within the Greater Thessaloniki Area, Greece, a Mediterranean territory which is characterised by high levels of photochemical air pollution and considerable agricultural activity. Ozone concentration fields for 2002 and for specific emission reduction scenarios for the year 2010 were estimated with the Ozone Fine Structure model in the area under consideration. Total economic damage to crops turns out to be significant and estimated to be approximately 43 M€ for the reference year. Production of cotton presents the highest economic loss, which is over 16 M€, followed by table tomato (9 M€), rice (4.2 M€), wheat (4 M€) and oilseed rape (2.8 M€) cultivations. Losses are not spread uniformly among farmers and the major losses occur in areas with valuable ozone-sensitive crops. The results are very useful for highlighting the magnitude of the total economic impacts of photochemical air pollution to the area’s agricultural sector and can potentially be used for comparison with studies worldwide. Furthermore, spatial analysis of the economic damage could be of importance for governmental authorities and decision makers since it provides an indicative insight, especially if the economic instruments such as financial incentives or state subsidies to farmers are considered.     

HRT对固定床厌氧反应器运行效果的影响

为了考察水力停留时间(HRT)对炭纤维载体固定床厌氧反应器运行效果的影响,在进水COD分别为20 000~25 000 mg/L和40 000~45 000 mg/L2个浓度范围下,研究了不同HRT对反应器运行效果的影响。结果表明,通过HRT的调整,在达到相同有机负荷(OLR)下,进水COD为20 000~25 000 mg/L的COD去除率和产气量,明显比进水COD为40 000~45 000 mg/L的运行效果好;进水COD为20 000~25 000 mg/L,HRT为14 h,相应的OLR为41.09 kgCOD/(m3.d)时,COD去除率仍然维持在68%以上,沼气容积产气率达到14.55 m3/(m3.d)。炭纤维载体固定床厌氧反应器具有较高的COD去除率、产气效率以及抵抗低pH、高负荷冲击的能力,运行过程中没有发生反应器堵塞的现象。     

可用于水体污染控制的氨氮转化菌筛选及部分降解特性的实验研究

从南京禄口水产养殖基地淡水鱼塘取淤泥作为分离菌株的土源,采用选择性富集培养法,从中分离到能以硫酸铵为氮源的菌株7株,对7个菌株进行氨氮降解实验,它们氨氮转化率分别为14.8%、19.7%、53.4%、94.2%、29.1%、63.5%和41.7%,其中AN-4菌株的转化率最高且生长良好。通过AN-4菌株16S rRNA基因序列分析以及生理生化方法,鉴定此菌株为克雷伯氏菌属(Klebsiellasp.)。对菌株AN-4转化氨氮的特性及温度、pH值、氨氮初始浓度和菌株接种量对其氨氮转化率的影响研究,结果表明,菌株AN-4降解氨氮的最适条件为:温度为30℃和pH值为8.0;当氨氮初始浓度为30mg/L时,AN-4菌株在24 h内的氨氮降解率可达85%以上,且能耐受高达200 mg/L的氨氮浓度;AN-4活化菌液浓度为108cfu/mL,当接种量为3×106cfu/mL时,AN-4菌株在24 h内的氨氮降解率为87.75%。综合上述结果,符合淡水养殖水环境条件,说明AN-4菌株适合在水产养殖中应用,为将菌株AN-4应用于水产养殖环境修复提供了理论依据。