Measures of preferred direction for environmental and ecological circular data

Circular or angular variables indicating direction or cyclical time can be of great interest to scientists studying ecology, biology or environmental issues. A common problem of interest in circular data is estimating a preferred direction and its corresponding distribution. This problem is complicated by the so-called “wrap-around effect” on the circle, which exists because there is no natural minimum or maximum. The usual statistics employed for linear data are inappropriate for directional data, as they do not account for its circular nature. Three choices for summarizing the preferred direction (the sample circular mean, sample circular median and a circular analog of the Hodges–Lehmann estimator) are discussed, with examples from environmental and ecological applications. Similar to the linear data case, the relative emphases of different methods sometimes yield different measures of preferred direction in the presence of outliers or lack of symmetry in the original data. Received: November 2003 / Revised: June 2004     

The effect of wind direction on cross-pollination in wind-pollinated GM crops.

In Europe, regulatory thresholds restrict adventitious GM (genetically modified) presence in conventional crops. Minimum distances for the spatial separation of fields are often recommended to reduce field-to-field cross-pollination to an acceptable level. Field trials are typically the basis for setting separation distances. However, using records of wind direction and speed from weather stations across Europe, we predict theoretically that field-to-field windborne cross-pollination in maize, oilseed rape, sugar beet, and rice varies greatly according to the relative orientation of the GM and non-GM fields. Furthermore, at a given site and orientation from a GM field, we predict that the cross-pollination rate varies substantially from year to year. Consequently, even replicated field trials may inaccurately estimate typical levels of cross-pollination and therefore distort our perception of the separation distances required to achieve sub-threshold adventitious GM presence. We propose methods to predict the likely range in levels of cross-pollination based on the limited data typically available from field trials. Additionally, we suggest suitable time lags between peak flowering in adjacent fields that could be introduced to reduce cross-pollination to a specified level.     

Prediction and cause investigation of ozone based on a double-stage attention mechanism recurrent neural network

● Used a double-stage attention mechanism model to predict ozone. ● The model can autonomously select the appropriate time series for forecasting. ● The model outperforms other machine learning models and WRF-CMAQ. ● We used the model to analyze the driving factors of VOCs that cause ozone pollution. Ozone is becoming a significant air pollutant in some regions, and VOCs are essential for ozone prediction as necessary ozone precursors. In this study, we proposed a recurrent neural network based on a double-stage attention mechanism model to predict ozone, selected an appropriate time series for prediction through the input attention and temporal attention mechanisms, and analyzed the cause of ozone generation according to the contribution of feature parameters. The experimental data show that our model had an RMSE of 7.71 μg/m³ and a mean absolute error of 5.97 μg/m³ for 1-h predictions. The DA-RNN model predicted ozone closer to observations than the other models. Based on the importance of the characteristics, we found that the ozone pollution in the Jinshan Industrial Zone mainly comes from the emissions of petrochemical enterprises, and the good generalization performance of the model is proved through testing multiple stations. Our experimental results demonstrate the validity and promising application of the DA-RNN model in predicting atmospheric pollutants and investigating their causes.     

Numerical simulations of the effect of building configurations and wind direction on fine particulate matters dispersion in a street canyon

To explore the effect of traffic emissions on air quality within street canyon, the wind flow and pollutant dispersion distribution in urban street canyons of different H/W, building gap and wind direction are studied and discussed by 3D computational fluid dynamics simulations. The largest PM_2.5 concentrations are 46.4, 37.5, 28.4 µg/m³ when x = ? 88, ? 19.3, ? 19.3 m in 1.5 m above the ground level and the ratio of H/W is 1:1, 1:2 and 2:1, respectively. The flow around the top of the building and clearance flow between the buildings in street canyon influence by different H/W, which affected the diffusion of fine particulate matters. The largest PM_2.5 concentrations are 88.1, 31.6 and 33.7 µg/m³ when x = 148.0, ? 92.3 and ? 186.7 m above the ground level of 1.5 m height and the building gap of 0, 20 and 40%, respectively. The air flows are cut by the clearance in the street canyons, and present the segmental characteristics. The largest PM_2.5 concentrations are 10.6, 11.2 and 16.0 µg/m³ when x = 165.3 m, x = 58.0 and 1.5 m above the ground level of 1.5 m height and wind direction of the parallel to the street, perpendicular to the street and southwest, respectively. Modelled PM_2.5 concentrations are basic agreement with measured PM_2.5 concentrations for southwest wind direction. These results can help analyze the difussion of PM_2.5 concentration in street canyons and urban planning.

     

The effect of pollution on crime: Evidence from data on particulate matter and ozone

We estimate the effect of short-term air pollution exposure (PM_2.5 and ozone) on several categories of crime, with a particular emphasis on aggressive behavior. To identify this relationship, we combine detailed daily data on crime, air pollution, and weather for an eight-year period across the United States. Our primary identification strategy employs extremely high dimensional fixed effects and we perform a series of robustness checks to address confounding variation between temperature and air pollution. We find a robust positive effect of increased air pollution on violent crimes, and specifically assaults, but no relationship between increases in air pollution and property crimes. The effects are present in and out of the home, at levels well below Ambient Air Pollution Standards, and PM_2.5 effects are strongest at lower temperatures. The results suggest that a 10% reduction in daily PM_2.5 and ozone could save $1.4 billion in crime costs per year, a previously overlooked cost associated with pollution.     

Eco-efficiency of urban material metabolism: a case study in Xiamen,China

In this research, the data envelopment analysis (DEA) model of measuring the eco-efficiency of urban material metabolism has been constructed based on the urban material input and output indicators. According to the data of Xiamen from 1985 to 2007, an empirical study was conducted to measure the eco-efficiency of urban material metabolism using DEA program. The results showed a general downtrend of Xiamen's eco-efficiency of material metabolism from 1985 to 2007, in which there were 15 efficient years and eight inefficient years. The eco-efficiency and urbanization rate of Xiamen was remarkably negatively correlated. Moreover, the results revealed at 4 years (1995, 2001, 2002 and 2003) there had been undesirable output slack (environmental pollution), and at 2 years (1991 and 1999) there had been desirable output slack (GDP), and at 8 years there had been input slack (water, land, food and energy), especially for water and energy. Finally, some suggestions on promoting the eco-efficiency of urban material metabolism were put forward.     

The impact of chemical lateral boundary conditions on CMAQ predictions of tropospheric ozone over the continental United States

A sensitivity study is performed to examine the impact of lateral boundary conditions (LBCs) on the NOAA-EPA operational Air Quality Forecast Guidance over continental USA. We examined six LBCS: the fixed profile LBC, three global LBCs, and two ozonesonde LBCs for summer 2006. The simulated results from these six runs are compared to IONS ozonesonde and surface ozone measurements from August 1 to 5, 2006. The choice of LBCs can affect the ozone prediction throughout the domain, and mainly influence the predictions in upper altitude or near inflow boundaries, such as the US west coast and the northern border. Statistical results shows that the use of global model predictions for LBCs could improve the correlation coefficients of surface ozone prediction over the US west coast, but could also increase the ozone mean bias in most regions of the domain depending on global models. In this study, the use of the MOZART (Model for Ozone And Related chemical Tracers) prediction for CMAQ (Community Multiscale Air Quality) LBC shows a better surface ozone prediction than that with fixed LBC, especially over the US west coast. The LBCs derived from ozonesonde measurements yielded better O₃ correlations in the upper troposphere.     

A CFD study of wind patterns over a desert dune and the effect on seed dispersion

In the Namib Desert seed distribution is greatly influenced by wind patterns. Existing literature regarding wind patterns over dunes focuses on two-dimensional simulations of flow over simplified dune structures. The three-dimensional geometries of the sand dunes suggests far more complex flow features exist, which are not captured by two-dimensional simulations. Computational fluid dynamics (CFD) was used to reproduce the three-dimensional near surface wind patterns around a dune with the aim to learn more about seed distribution. Field work included terrain mapping, wind speed, direction and temperature metering. The CFD results show the expected two-dimensional flow features of high pressure at the dune toe, low pressure at the crest and flow acceleration up windward slope. Also observed are some three-dimensional flow features such as a spiral vortex near the crest and transverse flow due to crest-line curvature of the dune. It was also observed how the wall shear stress differs due to the three-dimensional shape of the dune. The wall shear stress suggests that seed accumulation is more likely to occur behind trailing (down-wind) crest edges. Particle tracking showed how seeds tend to move over the dune crest and recirculate towards the crest on the lee-side. The study showed that adding the third dimension makes the simulations more complex, adds to computational requirements and increases simulation time but also provides vital flow information which is not possible with two-dimensional simulations.     

Development and case study of a new-generation model-VAT for analyzing the boundary conditions influence on atmospheric mercury simulation

Atmospheric models are essential tools to study the behavior of air pollutants. To interpret the complicated atmospheric model simulations, a new-generation Model Visualization and Analysis Tool (Model-VAT) has been developed for scientists to analyze the model data and visualize the simulation results. The Model-VAT incorporates analytic functions of conventional tools and enhanced capabilities in flexibly accessing, analyzing, and comparing simulated results from multi-scale models with different map projections and grid resolutions. The performance of the Model-VAT is demonstrated by a case study of investigating the influence of boundary conditions (BCs) on the ambient Hg formation and transport simulated by the CMAQ model over the Pearl River Delta (PRD) region. The alternative BC options are taken from (1) default time-independent profiles, (2) outputs from a CMAQ simulation of a larger nesting domain, and (3) concentration files from GEOS-Chem (re-gridded and re-projected using the Model-VAT). The three BC inputs and simulated ambient concentrations and deposition were compared using the Model-VAT. The results show that the model simulations based on the static BCs (default profile) underestimates the Hg concentrations by ~6.5%, dry depositions by ~9.4%, and wet depositions by ~43.2% compared to those of the model-derived (e. g. GEOS-Chem or nesting CMAQ) BCs. This study highlights the importance of model nesting approach and demonstrates that the innovative functions of Model-VAT enhances the efficiency of analyzing and comparing the model results from various atmospheric model simulations.

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Rising sea and threatened mangroves: a case study on stakeholders,engagement in climate change communication and non-formal education

Scientific consensus shows that the changes related to climate change are already occurring and will intensify in the future. This will likely result in significant alterations to coastal ecosystems such as mangroves, increase coastal hazards and affect lifestyles of coastal communities. There is increasing speculation that mangrove, a socio-economically important ecosystem, will become more fragile and sensitive to uncertain climate variability such as sea level rise. As a result, mangrove-dependent societies may find themselves trapped in a downward spiral of ecological degradation in terms of their livelihoods and life security. Strengthening the resilience capacity of coastal communities to help them cope with this additional threat from climate change and to ensure sustainability calls for immediate action. In this context, this paper critically examines the regional implications of expected sea level rise and threats to mangrove-dependent communities through a case study approach. The main objective is to highlight the requirement for climate change communication and education to impart information that will fulfil three expectations: (1) confer understanding; (2) assess local inference on climate change through a participatory approach; and (3) construct a framework for climate change awareness among mangrove-dependent communities through community-based non-formal climate change education. This scale of approach is attracting increasing attention from policymakers to achieve climate change adaptation and derive policies from a social perspective.     

A GIS-based approach to spatio-temporal analysis of environmental pollution in urban areas: A case study of Prague's environment extended by LIDAR data

Environmental pollution of urban areas is one of key factors that state authorities and local agencies have to consider in the decision-making process. To find a compromise among many criteria, spatial analysis extended by geostatistical methods and dynamic models has to be carried out. In this case, spatial analysis includes processing of a wide range of air, water and soil pollution data and possibly noise assessment and waste management data. Other spatial inputs consist of data from remote sensing and GPS field measurements. Integration and spatial data management are carried out within the framework of a geographic information system (GIS). From a modeling point of view, GIS is used mainly for the preprocessing and postprocessing of data to be displayed in digital map layers and visualized in 3D scenes. Moreover, for preprocessing and postprocessing, deterministic and geostatistical methods (IDW, ordinary kriging) are used for spatial interpolation; geoprocessing and raster algebra are used in multi-criteria evaluation and risk assessment methods. GIS is also used as a platform for spatio-temporal analyses or for building relationships between the GIS database and stand-alone modeling tools. A case study is presented illustrating the application of spatial analysis to the urban areas of Prague. This involved incorporating environmental data from monitoring networks and field measurements into digital map layers. Extra data inputs were used to represent the 3D concentration fields of air pollutants (ozone, NO₂) measured by differential absorption LIDAR. ArcGIS was used to provide spatial data management and analysis, extended by modeling tools developed internally in the ArcObjects environment and external modules developed with MapObjects. Ordinary kriging methods were employed to predict ozone concentrations in selected 3D locations together with estimates of variability. Higher ozone concentrations were found above crossroads with their heavy traffic than above the surrounding areas. Ozone concentrations also varied with height above the digital elevation model. Processed data, spatial analysis and models are integrated within the framework of the GIS project, providing an approach that state and local authorities can use to address environmental protection issues.     

Measuring the benefits of environmental change using a duality approach: The case of ozone and Illinois cash grain farms

The measurement of the economic effects of a changing environment can be complex and costly. This study uses a duality approach to quantify the relationship between ambient levels of ozone and economic damage on Illinois cash grain farms. Econometric estimates from producers' profit functions indicate a significant negative relationship between ozone and the levels of profit and output. These findings are consistent with results from more traditional ecosystem assessment procedures. Given the data and experimentation costs associated with traditional approaches, this study's results point to duality as a potentially powerful tool for assessing the economic effects of various environmental phenomena.     

村级农业生态经济系统的结构调控研究--以石辛庄为例

运用生态学原理,从结构元、结构链和结构网3个层次对石辛庄村级农业生态经济系统结构进行调控,并从生态效益、资源利用效益、环境效益和经济效益4个方面对该生态经济系统的调控效果作了评价.     

A study of peroxyacetyl nitrate at a rural site in Beijing based on continuous observations from 2015 to 2019 and the WRF-Chem model

•PAN concentrations at a rural site near Beijing were monitored from 2015 to 2019. •PAN concentrations exhibited high values in spring and low values in winter. •Anomalously southerlies induced extreme high PAN concentration in spring 2018. Peroxyacetyl nitrate (PAN) is one of the most important photochemical pollutants and has aroused much concern in China in recent decades. However, few studies described the long-term variations in PAN in China. In this study, we continuously monitored the PAN, O₃ and NO_x concentrations at a regional background site near Beijing from August 2015 to February 2019. Based on the observed concentrations and climate data, we analyzed the seasonal PAN variations. The results revealed that the monthly mean PAN concentration ranged from 0.33–2.41 ppb, with an average value of 0.94 ppb. The PAN concentration exhibited a distinct seasonal variation, with high values in spring and low values in winter. After analyzing the corresponding meteorological data, we found that stronger ultraviolet (UV) radiation, a relatively longer lifetime and a higher background PAN concentration contributed to the high PAN concentrations in spring. In addition, with the utilization of the WRF-Chem (Weather Research and Forecasting with Chemistry) model, the cause of the extremely high PAN concentration in spring 2018 was determined. The model results demonstrated that an anomalously low pressure and the southwesterly winds in northern China might be the main causes of the increased PAN concentration in Beijing and its surrounding area in spring 2018.     

基于道路网络特征的建设用地扩张及其对林地景观的影响——以深圳市为例

道路作为景观廊道,与建设用地景观、林地景观之间存在着复杂的生态学联系。在道路网络迅猛扩展的同时,深圳市建设用地迅速扩张,并对林地景观产生了一定影响。基于土地利用变更调查数据,对1996-2008年快速城市化地区深圳市道路密度与建设用地扩张强度、道路密度与建设用地-林地边界动态的相互关系进行了研究。结果表明,建设用地规模与道路密度、建设用地扩张强度与道路拓展强度呈相关关系,道路网络对城市的社会经济发展有着重要的支撑能力。另一方面,研究期间深圳市林地退化严重,与建设用地接触的单个林地斑块的平均边界长度（林建边界长度）增加,建设用地-林地边界呈现出复杂化的趋势。林建边界长度与道路密度成相关关系,道路密度小于2.5 km.km-2时,道路密度越大,林建边界长度越大,建设用地景观和林地景观的生态学作用越剧烈。     

The effect of conventional wastewater treatment on the levels of antimicrobial-resistant bacteria in effluent: a meta-analysis of current studies

Antimicrobial agents in the environment are a cause for concern. Antimicrobial drug residues and their metabolites reach the aquatic and terrestrial environment primarily through wastewater treatment plants (WWTP). In addition to the potential direct negative health and environmental effects, there is potential for the development of antimicrobial-resistant bacteria. Residue levels below the minimum inhibitory concentration for a bacterial species can be important in selection of resistance. There is uncertainty associated with resistance formation during WWTP processing. A meta-analysis study was carried out to analyse the effect of WWTP processing on the levels of antimicrobial-resistant bacteria within bacterial populations. An analysis of publications relating to multiple antimicrobial-resistant (MAR) bacteria (n?=?61), single antimicrobial-resistant (SAR) E. coli (n?=?81) and quinolone/fluoroquinolone-resistant (FR) bacteria (n?=?19) was carried out. The odds-ratio (OR) of MAR (OR?=?1.60, p?<?0.01), SAR (OR?=?1.33, p?<?0.01) and FR (OR?=?1.19, p?<?0.01) bacteria was determined. The results infer that WWTP processing results in an increase in the proportion of resistant bacteria in effluent, even though the overall bacterial population may have reduced (i.e. a reduction in total bacterial numbers but an increase in the percentage of resistant bacteria). The results support the need for further research into the development of antimicrobial-resistant strains and possible selective pressures operating in WWTPs.     

Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird

We assessed the occurrence of a common river bird, the Plumbeous Redstart Rhyacornis fuliginosus, along 180 independent streams in the Indian and Nepali Himalaya. We then compared the performance of multiple discrimant analysis (MDA), logistic regression (LR) and artificial neural networks (ANN) in predicting this species’ presence or absence from 32 variables describing stream altitude, slope, habitat structure, chemistry and invertebrate abundance. Using the entire data (=training set) and a threshold for accepting presence in ANN and LR set to P≥0.5, ANN correctly classified marginally more cases (88%) than either LR (83%) or MDA (84%). Model performance was assessed from two methods of data partitioning. In a ‘leave-one-out’ approach, LR correctly predicted more cases (82%) than MDA (73%) or ANN (69%). However, in a holdout procedure, all the methods performed similarly (73–75%). All methods predicted true absence (i.e. specificity in holdout: 81–85%) better than true presence (i.e. sensitivity: 57–60%). These effects reflect species’ prevalence (=frequency of occurrence), but are seldom considered in distribution modelling. Despite occurring at only 36% of the sites, Plumbeous Redstarts are one of the most common Himalayan river birds, and problems will be greater with less common species. Both LR and ANN require an arbitrary threshold probability (often P=0.5) at which to accept species presence from model prediction. Simulations involving varied prevalence revealed that LR was particularly sensitive to threshold effects. ROC plots (received operating characteristic) were therefore used to compare model performance on test data at a range of thresholds; LR always outperformed ANN. This case study supports the need to test species’ distribution models with independent data, and to use a range of criteria in assessing model performance. ANN do not yet have major advantages over conventional multivariate methods for assessing bird distributions. LR and MDA were both more efficient in the use of computer time than ANN, and also more straightforward in providing testable hypotheses about environmental effects on occurrence. However, LR was apparently subject to chance significant effects from explanatory variables, emphasising the well-known risks of models based purely on correlative data.     

The Trifinio Region: a case study of transboundary forest change in Central America

Strategic planning to increase forest cover in Central American transboundary areas of the Mesoamerican Biological Corridor requires understanding land-cover and land-use change trends and drivers. We estimated forest cover change from remotely sensed land-cover and land-use classifications from 1986, 2001, and 2010, in the tri-national Trifinio Region, bordering El Salvador, Guatemala, and Honduras. Our analysis spanned subnational, national, regional, and protected border areas. We determined correlations with direct drivers of deforestation, developing a multilevel linear regression model. Higher population density significantly correlated with deforestation; coffee, agroforestry, and pasture replaced forests. The tri-national park retained forests compared to neighboring areas, but additionality requires more research. The literature on drivers suggests similar processes and factors in other tropical regions. Forest cover governance efficacy is highly variable. Results indicate relationship between governance and forest cover though more comprehensive understanding of this complex region is needed to determine their causality.     

The effect of texture and irrigation on the soil moisture vertical-temporal variability in an urban artificial landscape: a case study of Olympic Forest Park in Beijing

Soil moisture variability in natural landscapes has been widely studied; however, less attention has been paid to its variability in the urban landscapes with respect to the possible influence of texture stratification and irrigation management. Therefore, a case study was carried out in the Beijing Olympic Forest Park to continuously monitor the soil in three typical profiles from 26 April to 11 November 2010. The texture stratification significantly affected the vertical distribution of moisture in the non-irrigated profile where moisture was mostly below field capacity. In the profile where irrigation was sufficient to maintain moisture above field capacity, gravity flow led to increased moisture with depth and thus eliminated the influence of texture. In the non-irrigated sites, the upper layer (above 80 cm) exhibited long-term moisture persistence with the time scale approximating the average rainfall interval. However, a coarse-textured layer weakened the influence of rainfall, and a fine-textured layer weakened the influence of evapotranspiration, both of which resulted in random noise-like moisture series in the deeper layers. At the irrigated site, frequent irrigation neutralized the influence of evapotranspiration in the upper layer (above 60 cm) and overshadowed the influence of rainfall in the deeper layer. As a result, the moisture level in the upper layer also behaved as a random noise-like series; whereas due to deep transpiration, the moisture of the deep layer had a persistence time-scale longer than a month, consistent with characteristic time-scales found for deep transpiration.     

城市湖泊流域面源污染的源-汇效应研究--以武汉市东湖为例

随着城市点源污染控制不断完善,城市面源污染已成为危害中国城市水体的重要污染源.由城市土地利用变化引起的地表径流污染是导致湖泊水质恶化和富营养化的主要因素,加强面源污染的源-汇效应研究是控制、管理流域污染的重要途径之一.研究选取中国最大的城市湖泊武汉市东湖为研究对象,运用遥感和GIS技术,进行流域划分和地类解译,建立土地利用基础数据库,后对水质统计数据进行定量化处理,通过建立了地类——湖泊水质关系模型,科学地分析了用地与水质的关系,并对城市湖泊水质的季节动态、影响因素进行了初步探索.研究结果表明土地利用和气候变化对流域内湖泊水质的影响显著.其中,农田和建设用地是城市湖泊面源污染的主要来源,绿地和坑塘水面能够有效的截流、吸收污染物,起到保护湖泊水质的作用;受降水、温度等气候因子的影响,湖泊水质状态呈现季节性波动.研究选取东湖为研究对象,研究面源污染的源-汇效应,具有代表性,对于更好保护城市湖泊,合理利用湖泊资源提供了理论依据.