Spatial modeling of rat bites and prediction of rat infestation in Peshawar valley using binomial kriging with logistic regression

In this study, we propose to develop a geostatistical computational framework to model the distribution of rat bite infestation of epidemic proportion in Peshawar valley, Pakistan. Two species Rattus norvegicus and Rattus rattus are suspected to spread the infestation. The framework combines strengths of maximum entropy algorithm and binomial kriging with logistic regression to spatially model the distribution of infestation and to determine the individual role of environmental predictors in modeling the distribution trends. Our results demonstrate the significance of a number of social and environmental factors in rat infestations such as (I) high human population density; (II) greater dispersal ability of rodents due to the availability of better connectivity routes such as roads, and (III) temperature and precipitation influencing rodent fecundity and life cycle.     

Modeling the spatial pattern of farmland using GIS and multiple logistic regression: a case study of Maotiao River Basin, Guizhou Province, China

Land use change is an important topic in the field of global environmental change and sustainable development. Land use change modeling has attracted substantial attention because it helps researchers understand the mechanisms of land use change and assists regulatory bodies in formulating relevant policies. Maotiao River Basin is located in the province of Guizhou, China, which has a developed agricultural industry in the karst mountain areas. This paper selected biophysical and social–economic factors as independent variables, and constructed a multiple logistic regression of farmland spatial distribution probability by random sampling. Then, by using GIS technology and integrating the 2000 data, this study predicted the farmland spatial pattern. When the predicted map was compared with the actual farmland map for 2000, we noted that 71% of the simulation is in accordance with the 2000 farmland pattern. The result satisfactorily proves the reasonability and applicability of our model.     

A hydrologic network supporting spatially referenced regression modeling in the Chesapeake Bay Watershed

The U.S. Geological Survey has developed a methodology for statistically relating nutrient sources and land-surface characteristics to nutrient loads of streams. The methodology is referred to as SPAtially Referenced Regressions On Watershed attributes (SPARROW), and relates measured stream nutrient loads to nutrient sources using nonlinear statistical regression models. A spatially detailed digital hydrologic network of stream reaches, stream-reach characteristics such as mean streamflow, water velocity, reach length, and travel time, and their associated watersheds supports the regression models. This network serves as the primary framework for spatially referencing potential nutrient source information such as atmospheric deposition, septic systems, point-sources, land use, land cover, and agricultural sources and land-surface characteristics such as land use, land cover, average-annual precipitation and temperature, slope, and soil permeability. In the Chesapeake Bay watershed that covers parts of Delaware, Maryland, Pennsylvania, New York, Virginia, West Virginia, and Washington D.C., SPARROW was used to generate models estimating loads of total nitrogen and total phosphorus representing 1987 and 1992 land-surface conditions. The 1987 models used a hydrologic network derived from an enhanced version of the U.S. Environmental Protection Agency's digital River Reach File, and course resolution Digital Elevation Models (DEMs). A new hydrologic network was created to support the 1992 models by generating stream reaches representing surface-water pathways defined by flow direction and flow accumulation algorithms from higher resolution DEMs. On a reach-by-reach basis, stream reach characteristics essential to the modeling were transferred to the newly generated pathways or reaches from the enhanced River Reach File used to support the 1987 models. To complete the new network, watersheds for each reach were generated using the direction of surface-water flow derived from the DEMs. This network improves upon existing digital stream data by increasing the level of spatial detail and providing consistency between the reach locations and topography. The hydrologic network also aids in illustrating the spatial patterns of predicted nutrient loads and sources contributed locally to each stream, and the percentages of nutrient load that reach Chesapeake Bay.     

Dynamic regression modeling of daily nitrate-nitrogen concentrations in a large agricultural watershed

Nitrate-nitrogen concentrations in rivers represent challenges for water supplies that use surface water sources. Nitrate concentrations are often modeled using time-series approaches, but previous efforts have typically relied on monthly time steps. In this study, we developed a dynamic regression model of daily nitrate concentrations in the Raccoon River, Iowa, that incorporated contemporaneous and lags of precipitation and discharge occurring at several locations around the basin. Results suggested that 95 % of the variation in daily nitrate concentrations measured at the outlet of a large agricultural watershed can be explained by time-series patterns of precipitation and discharge occurring in the basin. Discharge was found to be a more important regression variable than precipitation in our model but both regression parameters were strongly correlated with nitrate concentrations. The time-series model was consistent with known patterns of nitrate behavior in the watershed, successfully identifying contemporaneous dilution mechanisms from higher relief and urban areas of the basin while incorporating the delayed contribution of nitrate from tile-drained regions in a lagged response. The first difference of the model errors were modeled as an AR(16) process and suggest that daily nitrate concentration changes remain temporally correlated for more than 2 weeks although temporal correlation was stronger in the first few days before tapering off. Consequently, daily nitrate concentrations are non-stationary, i.e. of strong memory. Using time-series models to reliably forecast daily nitrate concentrations in a river based on patterns of precipitation and discharge occurring in its basin may be of great interest to water suppliers.     

Spatio-temporal variations of organic carbon and chlorophyll degradation products in the surficial sediments of Izmir Bay (Aegean Sea/Turkey)

The aim of this research is to determine the effects of Izmir Big Channel Waste Water Treatment Project on the sediment quality of Izmir Bay. Wastewater treatment improves the water quality. However, sediment does not respond to this treatment as fast as water column. Monitoring of bottom water and sediment quality is necessary for identification of the recovery of the whole ecosystem. For this purpose, bottom water and sediment samples were collected from three stations which are located in the middle and inner parts of the Izmir Bay on a monthly basis between January 2003 and December 2003. Values measured at stations ranged between; 0.54-12.82 microg/L for chlorophyll-a, 0.09-9.32 microg/L for phaeopigment, 0.05-1.91 mg/L for particulate organic carbon in bottom waters, 11.88-100.29 microg/g for chlorophyll degradation products and 1.12-5.39% for organic carbon in sediment samples. In conclusion, it was found that grazing activity explained carbon variations in sediment at station 2, but at station 1 and station 3 carbon variations in sediment were not related to autochthonous biological processes.     

Similarities and differences of metal distributions in the tissues of molluscs by using multivariate analyses

Multivariate analysis including correlation, multiple stepwise linear regression, and cluster analyses were applied to investigate the heavy metal concentrations (Cd, Cu, Fe, Ni, Pb, and Zn) in the different parts of bivalves and gastropods. It was also aimed to distinguish statistically the differences between the marine bivalves and the gastropods with regards to the accumulation of heavy metals in the different tissues. The different parts of four species of bivalves and four species of gastropods were obtained and analyzed for heavy metals. The multivariate analyses were then applied on the data. From the multivariate analyses conducted, there were correlations found between the soft tissues of bivalves and gastropods, but none was found between the shells and the soft tissues of most of the molluscs (except for Cerithidea obtusa and Puglina cochlidium). The significant correlations (P < 0.05) found between the soft tissues were further complemented by the multiple stepwise linear regressions where heavy metals in the total soft tissues were influenced by the accumulation in the different types of soft tissues. The present study found that the distributions of heavy metals in the different parts of molluscs were related to their feeding habits and living habitats. The statistical approaches proposed in this study are recommended for use in biomonitoring studies, since multivariate analyses can reduce the cost and time involved in identifying an effective tissue to monitor the heavy metal(s) bioavailability and contamination in tropical coastal waters.     

Land use and land cover (LULC) of the Republic of the Maldives: first national map and LULC change analysis using remote-sensing data

The Maldives islands in recent decades have experienced dramatic land-use change. Uninhabited islands were turned into new resort islands; evergreen tropical forests were cut, to be replaced by fields and new built-up areas. All these changes happened without a proper monitoring and urban planning strategy from the Maldivian government due to the lack of national land-use and land-cover (LULC) data. This study aimed to realize the first land-use map of the entire Maldives archipelago and to detect land-use and land-cover change (LULCC) using high-resolution satellite images and socioeconomic data. Due to the peculiar geographic and environmental features of the archipelago, the land-use map was obtained by visual interpretation and manual digitization of land-use patches. The images used, dated 2011, were obtained from Digital Globe’s WorldView 1 and WorldView 2 satellites. Nine land-use classes and 18 subclasses were identified and mapped. During a field survey, ground control points were collected to test the geographic and thematic accuracy of the land-use map. The final product’s overall accuracy was 85%. Once the accuracy of the map had been checked, LULCC maps were created using images from the early 2000s derived from Google Earth historical imagery. Post-classification comparison of the classified maps showed that growth of built-up and agricultural areas resulted in decreases in forest land and shrubland. The LULCC maps also revealed an increase in land reclamation inside lagoons near inhabited islands, resulting in environmental impacts on fragile reef habitat. The LULC map of the Republic of the Maldives produced in this study can be used by government authorities to make sustainable land-use planning decisions and to provide better management of land use and land cover.     

Uptake of heavy metals by some edible vegetables irrigated using wastewater: a preliminary study in Accra, Ghana

The heavy metals (Fe, Zn, Pb, Ni, Cr, Co, and Cd) burden in wastewater, soil, and vegetable samples from a wastewater irrigated farm located at KorleBu, Accra has been investigated. Flame atomic absorption spectrometry after microwave digestion using a combination of HNO₃, HCl, and H₂O₂ (for water), and HNO₃ and HCl (for soil and vegetables). The mean concentrations (in milligrams per kilogram) of heavy metals in the soil samples were in the order of Fe (171?±?5.22)?>?Zn (36.06?±?4.54)?>?Pb (33.35?±?35.62)?>?Ni (6.31?±?8.15)?>?Cr (3.40?±?3.63)?>?Co (1.36?±?0.31)?>?Cd (0.43?±?0.24), while the vegetables were in the order of Fe (183.11?±?161.2)?>?Zn (5.38?±?3.50)?>?Ni (3.52?±?1.27)?>?Pb (2.49?±?1.81)?>?Cr (1.46?±?0.51)?>?Co (0.66?±?0.25)?>?Cd (0.36?±?0.15). The bioconcentration factors suggest environmental monitoring for the heavy metals as follows: Cd (0.828), Cr (0.431), Ni (0.558), Co (0.485), and Fe (1.067). Estimated daily intakes were very low for both children and adults except Fe (0.767 mg/kg/day) in children. The population that consume vegetables from the study area were, however, estimated to be safe based on the results obtained from the health risk index, which were all?<?<1. The sodium absorption ratio according to FAO (1985) classifications indicate that the wastewater in the study area is unsuitable for irrigation purposes.     

A computer-based program for the assessment of water-induced contamination in irrigated lands

The non-point characteristic of agrarian contamination hinders its quantification and assignation to a specific territory. The objectives of this work were to unify methodological criteria for agro-environmental evaluation and to propose indices to quantify irrigation-induced contamination. The computer program Irrigation Land Environmental Evaluation Tool (in Spanish, EMR; http://www.jcausape.es/investigacion/EMR.htm ) was developed to evaluate the quality of irrigation and the agro-environmental impacts, based on the water, salt, and nitrate balances in the hydrological irrigation basins. The behavior of the proposed indices was analyzed using data registered in various irrigation districts in the Ebro valley (Spain). The Salt and Nitrate Contamination Indices (SCI and NCI, respectively) were based on the unitary mass of exported pollutants, corrected by the "natural and socioeconomic" conditions of the irrigation districts evaluated. SCI and NCI were related to water and nitrogen use, key factors in minimizing contamination. SCI and NCI admit a greater mass of exported pollutants in disadvantaged irrigation districts, which does not allow the exclusion of adequate management in any evaluated irrigation lands. EMR is a user-friendly tool at the service of the agro-environmental surveillance of irrigation lands.     

A hydrochemical elucidation of the groundwater composition under domestic and irrigated land in Jaipur City

The study area Jaipur, the capital of Rajasthan, is one of the famous metropolises in India. In order to know the suitability of groundwater for domestic and irrigation purposes in Jaipur City, groundwater samples were composed of 15 stations during post-monsoon time of the year 2007–2008 (Nov 2007 to Feb 2008) and were analyzed for physicochemical characters. The physicochemical parameters of groundwater participate a significant role in classifying and assessing water quality. A preliminary characterization, carried out using the piper diagram, shows the different hydrochemistry of the sampled groundwater. This diagram shows that most of the groundwater samples fall in the field of calcium-magnesium-chloride-sulfate type (such water has permanent hardness) of water. Data are plotted on the US Salinity Laboratory diagram, which illustrates that most of the groundwater samples fall in the field of C2S1 and C3S1, which can be used for irrigation on almost all type of soil with little danger of exchangeable sodium. Based on the analytical results, chemical indices like %Na, SAR, and RSC were calculated which show that most of the samples are good for irrigation.     

Compartment modeling of MTBE in the generic environment and estimations of the aquatic MTBE input in Germany using the EQC model

The use of the gasoline additive methyl tert-butyl ether (MTBE) has caused serious concern about groundwater and surface water contamination. The behavior of MTBE in the two most relevant compartments, surface water and air in a generic environment and in a simulated German environment is investigated using the equilibrium criterion (EQC) model. Due to lack of literature data, the half-life time of MTBE in river water is estimated to about 80-120 d (105 d) at 18 degrees C and roughly 1.5 a (year)(533 d) at 4 degrees C from a batch experiment. The EQC model considers four compartments, air, surface water, soil and sediment in an environment of typically 100,000 km2 with about 10% of the area covered with water. The user can progress through the tiered sequence of Level I to III with increasing complexity which reveals more information about the the fate of the considered chemical. The equilibrium mass distribution of MTBE calculated with the Level I model shows that 87% partitions into air and 13% into surface water at 10 degrees C. The results of the Level II calculations indicate that 50% of MTBE in the air is transported from the system and 38% in the air is degraded at 10 degrees C. The resulting total persistence time of 3 d for MTBE in the generic environment of the Level II model can be compared to the calculated value for chlorobenzene. The MTBE input into water is significantly more sensitive to the 'mode of entry' than input into air. The MTBE concentration in surface water is almost exclusively the result of direct emission into water, whereas the atmosphere can additionally be loaded by volatilization from water. The total aquatic MTBE emission in Germany and the average MTBE concentration in German surface waters were roughly estimated to 20-80 t a(-1) (tons per year)(50 t a(-1)) and 50 ng L(-1), respectively. Surface water concentrations calculated with the underlying assumptions of the model can neither be explained by exposure through waste water and industrial effluents nor with an estimated loss of industrially used MTBE in Germany. For the year-round scenario at 10 degrees C, MTBE concentrations of 19 ng L(-1) (surface water) and 167 ng m(-3) (air) result. However, it remains unclear whether the assumptions of the model, the lack of analyses from industrial effluents or both are responsible for the difference. Additional aquatic emission sources could result from gasoline transport on and storage near rivers. The comparison of winter and summer scenarios shows that in summer, atmospheric (25%) and aqueous (50%) concentrations are lower than in winter due to higher degradation rates.     

The LUCAS topsoil database and derived information on the regional variability of cropland topsoil properties in the European Union

Approximately 20,000 topsoil samples were collected in 25 European Union (EU) Member States (EU-27 except Bulgaria and Romania) with the aim to produce the first coherent pan-European physical and chemical topsoil database, which can serve as baseline information for an EU wide harmonized soil monitoring. The soil sampling was undertaken within the frame of the Land Use/Land Cover Area Frame Survey (LUCAS), a project to monitor changes in the management and character of the land surface of the EU. Soil samples have been analysed for basic soil properties, including particle size distribution, pH, organic carbon, carbonates, NPK, cation exchange capacity (CEC) and multispectral signatures. Preliminary studies show the outstanding potential of the dataset for enhancing the knowledge base on soils in the EU. The current paper provides an introduction to the LUCAS Topsoil 2009 project and provides an example of data applicability for cropland assessment by highlighting initial results for regional and national comparisons.     

Water quality assessment using integrated modeling and monitoring in Narva Bay, Gulf of Finland

A coupled three-dimensional hydrodynamic–ecological model was used for the assessment of water quality in Narva Bay during one biologically active season. Narva Bay is located in the south-eastern Gulf of Finland. Narva River with a catchment’s area covering part of Russia and Estonia discharges water and nutrients to Narva Bay. The ecological model includes phytoplankton carbon, nitrogen and phosphorus, chlorophyll a, zooplankton, detritus carbon, nitrogen and phosphorus, inorganic nitrogen, inorganic phosphorus and dissolved oxygen as state variables. Both the hydrodynamic and ecosystem models were validated using a limited number of measurements. The hydrodynamic model validation included comparison of time series of currents and temperature and salinity profiles. The ecological model results were compared with the monitoring data of phytoplankton biomass, total nitrogen and phosphorus and dissolved oxygen. The comparison of hydrodynamic parameters, phytoplankton biomass, surface layer total phosphorus and dissolved oxygen and near-bottom layer total nitrogen was reasonable. Time series of spatially mean values and standard deviations of selected parameters were calculated for the whole Narva Bay. Combining model results and monitoring data, the characteristic concentrations of phytoplankton biomass, total nitrogen and phosphorus and near-bottom dissolved oxygen were estimated. Phytoplankton biomass and total phosphorus showed seasonal variations, of 0.6–1.1 and 0.022–0.032 mg/l, respectively, during spring bloom, 0.1–0.3 and 0.015–0.025 mg/l in summer and 0.2–0.6 and 0.017–0.035 mg/l during autumn bloom. Total nitrogen and near-bottom oxygen concentrations were rather steady, being 0.25–0.35 and 2–6 mg/l, respectively. The total nitrogen and phosphorus concentrations show that according to the classification of Estonian coastal waters, Narva Bay water belongs to a good water quality class.     

Integrated groundwater resource management in Indus Basin using satellite gravimetry and physical modeling tools

Reliable and frequent information on groundwater behavior and dynamics is very important for effective groundwater resource management at appropriate spatial scales. This information is rarely available in developing countries and thus poses a challenge for groundwater managers. The in situ data and groundwater modeling tools are limited in their ability to cover large domains. Remote sensing technology can now be used to continuously collect information on hydrological cycle in a cost-effective way. This study evaluates the effectiveness of a remote sensing integrated physical modeling approach for groundwater management in Indus Basin. The Gravity Recovery and Climate Experiment Satellite (GRACE)-based gravity anomalies from 2003 to 2010 were processed to generate monthly groundwater storage changes using the Variable Infiltration Capacity (VIC) hydrologic model. The groundwater storage is the key parameter of interest for groundwater resource management. The spatial and temporal patterns in groundwater storage (GWS) are useful for devising the appropriate groundwater management strategies. GRACE-estimated GWS information with large-scale coverage is valuable for basin-scale monitoring and decision making. This frequently available information is found useful for the identification of groundwater recharge areas, groundwater storage depletion, and pinpointing of the areas where groundwater sustainability is at risk. The GWS anomalies were found to favorably agree with groundwater model simulations from Visual MODFLOW and in situ data. Mostly, a moderate to severe GWS depletion is observed causing a vulnerable situation to the sustainability of this groundwater resource. For the sustainable groundwater management, the region needs to implement groundwater policies and adopt water conservation techniques.     

Spatio-temporal changes in agrochemical inputs and the risk assessment before and after the grain-for-green policy in China

China’s Grain-For-Green Policy (GFGP) of returning marginal cropland to forest or grassland is one of the most important large-scale initiatives to combat land degradation in its ecologically vulnerable regions. In order to maintain and increase crop production from decreasing areas of cropland, substantial spatio-temporal changes in agrochemical inputs have occurred, which have strongly influenced the ecological and environmental status of land in China. Based on the agrochemical inputs (chemical fertilizer, pesticide, plastic sheeting, and agricultural diesel oil) at the provincial level between 1993 and 2009, cluster analysis and gravity center modeling were used to trace these spatio-temporal changes. A regional comparative study was also undertaken to investigate the changes in the relative size of agrochemical inputs in the eastern, central, and western regions of China. It was found that the agrochemical inputs increased considerably at the nation level after the GFGP, which in order of increasing rate were: plastic sheeting > agricultural diesel oil > pesticide > chemical fertilizer. The gravity centers of agrochemical inputs moved substantially towards the northwest or west during the latter period of GFGP and regional comparative analysis showed that the agrochemical inputs increased substantially in the western region between 2004 and 2009. The ecological degradation caused by the expansion of the area devoted to crop production in the western region and the potential risk of agricultural non-point pollution caused by the increasing agrochemical inputs are the main factors restricting this area’s sustainable development.     

Transformation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques

Analysis and forecasting of air quality parameters are important topics of atmospheric and environmental research today due to the health impact caused by air pollution. This study examines transformation of nitrogen dioxide (NO₂) into ozone (O₃) at urban environment using time series plot. Data on the concentration of environmental pollutants and meteorological variables were employed to predict the concentration of O₃ in the atmosphere. Possibility of employing multiple linear regression models as a tool for prediction of O₃ concentration was tested. Results indicated that the presence of NO₂ and sunshine influence the concentration of O₃ in Malaysia. The influence of the previous hour ozone on the next hour concentrations was also demonstrated.