Appraisal of measurement methods, chemical composition and sources of fine atmospheric particles over six different areas of Northern Belgium

Daily and seasonal variation in the total elemental, organic carbon (OC) and elemental carbon (EC) content and mass of PM_2.5 were studied at industrial, urban, suburban and agricultural/rural areas. Continuous (optical Dustscan, standard tapered element oscillating micro-balance (TEOM), TEOM with filter dynamics measurement system), semi-continuous (Partisol filter-sampling) and non-continuous (Dekati-impactor sampling and gravimetry) methods of PM_2.5 mass monitoring were critically evaluated. The average elemental fraction accounted for 2-6% of the PM_2.5 mass measured by gravimetry. Metals, like K, Mn, Fe, Cu, Zn and Pb were strongly inter-correlated, also frequently with non-metallic elements (P, S, Cl and/or Br) and EC/OC. A high OC/EC ratio (2-9) was generally observed. The total carbon content of PM_2.5 ranged between 3 and 77% (averages: 12-32%), peaking near industrial/heavy trafficked sites. Principal component analysis identified heavy oil burning, ferrous/non-ferrous industry and vehicular emissions as the main sources of metal pollution.     

石油石化企业温室气体清单编制简析

文章通过简要阐述石油石化企业温室气体清单的编制全过程,确定石油石化企业温室气体排放种类、识别主要温室气体排放源、列举重点生产过程中温室气体排放量的核算方法,为石油石化企业实现温室气体减排和控制工作、为完成国务院规定的到2020年我国单位国内生产总值二氧化碳排放强度比2005年下降40%~45%的约束性指标,奠定数据理论基础。     

Combining a finite mixture distribution model with indicator kriging to delineate and map the spatial patterns of soil heavy metal pollution in Chunghua County, central Taiwan

This study identifies the natural background, anthropogenic background and distribution of contamination caused by heavy metal pollutants in soil in Chunghua County of central Taiwan by using a finite mixture distribution model (FMDM). The probabilities of contaminated area distribution are mapped using single-variable indicator kriging and multiple-variable indicator kriging (MVIK) with the FMDM cut-off values and regulation thresholds for heavy metals. FMDM results indicate that Cr, Cu, Ni and Zn can be individually fitted by a mixture model representing the background and contamination distributions of the four metals in soil. The FMDM cut-off values for contamination caused by the metals are close to the regulation thresholds, except for the cut-off value of Zn. The receiver operating characteristic (ROC) curve validates that indicator kriging and MVIK with FMDM cut-off values can reliably delineate heavy metals contamination, particularly for areas lacking background information and high heavy metal concentrations in soil.     

Using multivariate regression modeling for sampling and predicting chemical characteristics of mixed waste in old landfills

Municipal solid waste landfills pose a threat on environment and human health, especially old landfills which lack facilities for collection and treatment of landfill gas and leachate. Consequently, missing information about emission flows prevent site-specific environmental risk assessments. To overcome this gap, the combination of waste sampling and analysis with statistical modeling is one option for estimating present and future emission potentials. Optimizing the tradeoff between investigation costs and reliable results requires knowledge about both: the number of samples to be taken and variables to be analyzed.This article aims to identify the optimized number of waste samples and variables in order to predict a larger set of variables. Therefore, we introduce a multivariate linear regression model and tested the applicability by usage of two case studies. Landfill A was used to set up and calibrate the model based on 50 waste samples and twelve variables. The calibrated model was applied to Landfill B including 36 waste samples and twelve variables with four predictor variables.The case study results are twofold: first, the reliable and accurate prediction of the twelve variables can be achieved with the knowledge of four predictor variables (Loi, EC, pH and Cl). For the second Landfill B, only ten full measurements would be needed for a reliable prediction of most response variables. The four predictor variables would exhibit comparably low analytical costs in comparison to the full set of measurements. This cost reduction could be used to increase the number of samples yielding an improved understanding of the spatial waste heterogeneity in landfills.Concluding, the future application of the developed model potentially improves the reliability of predicted emission potentials. The model could become a standard screening tool for old landfills if its applicability and reliability would be tested in additional case studies.     

Biomarker responsiveness in different tissues of caged Ruditapes philippinarum and its use within an integrated sediment quality assessment

Biomarkers comprising activities of biotransformation enzymes (ethoxyresorufin-O-deethylase -EROD-, dibenzylfluorescein dealkylase -DBF-, glutathione S-transferase -GST), antioxidant enzymes (glutathione reductase -GR- and glutathione peroxidase -GPX), lipid peroxidation -LPO- and DNA strand breaks were analyzed in the clam Ruditapes philippinarum caged at Cádiz Bay, Santander Bay and Las Palmas de Gran Canaria (LPGC) Port (Spain). Sediments were characterized. Digestive gland was the most sensitive tissue to sediment contamination. In Cádiz Bay, changes in LPO regarding day 0 were related with metals. In LPGC Port, DBF, EROD, and GST activity responses suggested the presence of undetermined contaminants which might have led to DNA damage. In Santander Bay, PAHs were related with EROD activity, organic and metal contamination was found to be associated with GR and GST activities and DNA damage presented significant (p < 0.05) induction. R. philippinarum was sensitive to sediment contamination at biochemical level. Biomarkers allowed chemical exposure and sediment quality assessment.     

Substituting missing data in compositional analysis

Multivariate analysis of environmental data sets requires the absence of missing values or their substitution by small values. However, if the data is transformed logarithmically prior to the analysis, this solution cannot be applied because the logarithm of a small value might become an outlier. Several methods for substituting the missing values can be found in the literature although none of them guarantees that no distortion of the structure of the data set is produced. We propose a method for the assessment of these distortions which can be used for deciding whether to retain or not the samples or variables containing missing values and for the investigation of the performance of different substitution techniques. The method analyzes the structure of the distances among samples using Mantel tests. We present an application of the method to PCDD/F data measured in samples of terrestrial moss as part of a biomonitoring study.     

A Comprehensive Python Toolkit for Accessing High‐Throughput Computing to Support Large Hydrologic Modeling Tasks

The National Flood Interoperability Experiment (NFIE) was an undertaking that initiated a transformation in national hydrologic forecasting by providing streamflow forecasts at high spatial resolution over the whole country. This type of large‐scale, high‐resolution hydrologic modeling requires flexible and scalable tools to handle the resulting computational loads. While high‐throughput computing (HTC) and cloud computing provide an ideal resource for large‐scale modeling because they are cost‐effective and highly scalable, nevertheless, using these tools requires specialized training that is not always common for hydrologists and engineers. In an effort to facilitate the use of HTC resources the National Science Foundation (NSF) funded project, CI‐WATER, has developed a set of Python tools that can automate the tasks of provisioning and configuring an HTC environment in the cloud, and creating and submitting jobs to that environment. These tools are packaged into two Python libraries: CondorPy and TethysCluster. Together these libraries provide a comprehensive toolkit for accessing HTC to support hydrologic modeling. Two use cases are described to demonstrate the use of the toolkit, including a web app that was used to support the NFIE national‐scale modeling.     

The multifunctionality of urban farming: perceived benefits for neighbourhood improvement

Urban farming – a type of urban agriculture focused on entrepreneurial food production – serves multiple functions in neighbourhoods; yet these are not well delineated. Expectations for urban farming often centre on traditional measures of economic development, potentially overlooking other benefits. Through a qualitative case study conducted in Baltimore, Maryland, we sought to understand community perceptions regarding the ways in which urban farms can benefit cities. Interviews with residents, neighbourhood leaders, and urban farmers in three residential neighbourhoods with urban farms revealed the pathways by which community members view farms as improving neighbourhoods. Benefits stemmed from four primary changes urban farms made to study neighbourhoods: creation of public projects welcoming involvement, physical improvement of degraded space, production of local food, and creation of new businesses. These changes led to multiple perceived benefits including increased social connectedness, a transformed physical landscape, improved neighbourhood reputation, increased access to fresh produce, and educational, youth development, and employment opportunities. Our findings demonstrate the importance of a multifunctional paradigm that accounts for social and educational functions in assessing the value of urban farming and bring empirical evidence to the concept of multifunctional agriculture. Urban farms with strong social aims may appear to contribute little to economic development if measured using traditional indicators of success such as job creation or fiscal impacts, but provide numerous other benefits for community development.     

Cognitive attributes and aesthetic preferences in assessment and differentiation of landscapes

The increasing pace and scale of landscape changes involve objective measurements in order to estimate the effects of changes on people's landscape preferences in a meaningful way. In the literature, some attempts have been made to provide a more conceptual base related to landscape preferences. These concepts and their indicators need to be tested empirically in different contexts and landscape types. In the present study, different items related to theoretical concepts of both aesthetic preference and cognitive rating were examined. They were combined in an in situ questionnaire, which was conducted among undergraduate students in geography during two different field excursions. Stimuli consisted of 11 landscape vistas selected during the excursions. All vistas represent rather rural landscapes but they vary with regard to relief, degree of urbanisation, and degree of agricultural land use. Statistical analysis of all data yielded significant correlations between aesthetic and cognitive ratings. However, these correlations did not appear to be very strong. When considering landscape vistas separately, the relations between all cognitive ratings seemed to vary. Further, not all cognitive aspects had an equal predicting value for aesthetic preference. Moreover, this predicting value appeared to vary between different landscape vistas. The groups of interrelated cognitive aspects could not be associated consistently with theoretical concepts. The results demonstrated the inconsistencies existing between the contents of the theoretical concepts and the indicators found within the landscape. The findings argued for the necessity to distinguish between different ratings and landscape types instead of using unitary preference measures and generalized data when studying landscape preference.     

Monitoring Environmental Impact in the Upper Sonoran Lifestyle: A New Tool for Rapid Ecological Assessment

Characterized by expensive housing, high socioeconomic status, and topographic relief, Upper Sonoran Lifestyle communities are found primarily along the Wildland-Urban Interface (WUI) in the Phoenix, Arizona metro area. Communities like these sprawl into the wildlands in the United States Southwest, creating a distinct urban fringe. This article, through locational comparison, introduces and evaluates a new field assessment tool for monitoring anthropogenic impact on soil–vegetation interactions along the well-maintained multi-use recreational trails in Upper Sonoran Lifestyle region. Comparing data from randomly selected transects along other multi-use trails with data from a control site revealed three key indicators of anthropogenic disturbances on soil–vegetation interactions: soil disturbance, vegetation disturbance, and vegetation density. Soil and vegetation disturbance displayed an average distance decay exponent factor of −0.60, while vegetation density displayed a reverse decay average of 0.60. Other important indicators of disturbance included vegetation type, biological soil crusts, and soil bulk density. The predictive ability of this new field tool enhances its applicability, offering a powerful rapid ecological assessment method for monitoring long-term anthropogenic impact in the Upper Sonoran Lifestyle, and other sprawling cities along the WUI.