Coordination Of Information On The Environment Research Articles

A comprehensive environmental exposure indicator and respiratory health in asthmatic children: a case study

AbstractThe primary goal of asthma management is to achieve and maintain asthma control, which can be influenced by environmental factors. This longitudinal study aimed to construct a comprehensive environmental indicator to predict asthma control in children with asthma in Palermo, Italy. The study included 179 asthmatic children aged 5–16 years. The Normalized Difference Vegetation Index (NDVI) was used to measure green cover, and the Coordination of Information on the Environment (CORINE) framework was used to assess land use based on each home address. A land use regression (LUR) model centered on the home address estimated NO2 exposure for each child using GIS. An environmental indicator, including environmental and personal exposure, was formulated using an additive value model approach. A logistic regression mixed model assessed the association between the environmental indicator and uncontrolled asthma. A probability map of uncontrolled asthma was constructed. In conclusion, a comprehensive environmental indicator proved effective in identifying areas at higher and lower risk of uncontrolled asthma.

Soil Erosion Risk Assessment in The Niğde Using Corine Model

Soil erosion risk was calculated using the coordination of information on the environment (CORINE) model in this study. The aim of the study is to determine the soil erosion risk of Niğde province, taking into account soil properties, slope and land use. Potential (PSER) and actual soil erosion risks (ASER) were determined using factors which were soil properties, slope, climatic factors, and land cover data. Data were produced using Arc-GIS 10.3 software, and results were obtained on these maps. 34.72% of the soils were classified as moderately actual soil erosion risk, which located in the eastern and southeast part of the study area.51.66% and 13.62% of the soils were classified as low and high actual soil erosion risk, respectively. Areas which have low actual soil erosion risk are located in the middle part, and areas which have high actual soil erosion risk are located in the northwest part of the area. The areas which were categorized as low potential soil erosion risk were increased from 23.52% to 51.66% in the actual soil erosion risk, after combining the land cover map. On the other hand, the total areas classified as high and moderate actual soil erosion risk decreased from 76.48% to 48.34% in the actual soil erosion risk due to land cover types. Soil texture, land cover, and slope are the most important factors that affect erosion risk. This study indicated that the CORINE model integrated with GIS (Geographic Information Systems) and RS (Revised Universal Soil Loss Equation) has a very effective and accurate potential for soil erosion risk assessment.

Near-real-time CO2 fluxes from CarbonTracker Europe for high-resolution atmospheric modeling

Abstract. We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe. We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands). We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE. We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures (“plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well. We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at https://doi.org/10.18160/20Z1-AYJ2 (van der Woude, 2022a).

Effects of mosquito resting site temperatures on the estimation of pathogen development rates in near-natural habitats in Germany

BackgroundEnvironmental temperature is a key driver for the transmission risk of mosquito-borne pathogens. Epidemiological models usually relate to temperature data from standardized weather stations, but these data may not capture the relevant scale where mosquitoes experience environmental temperatures. As mosquitoes are assumed to spend most of their lifetime in resting sites, we analysed mosquito resting site patterns and the associated temperatures in dependence on the resting site type, resting site height and the surrounding land use.MethodsThe study was conducted in 20 areas in near-natural habitats in Germany. Ten areas were studied in 2017, and another 10 in 2018. Each study area consisted of three sampling sites, where we collected mosquitoes and microclimatic data in artificial (= garden pop-up bags) and natural resting sites at three height levels between 0 and 6 m. Land use of the study sites was characterized as forest and meadows based on reclassified information of the CORINE (Coordination of Information on the Environment) Land Cover categories. The hourly resting site temperatures and the data from the nearest weather station of the German meteorological service were used to model the duration of the extrinsic incubation period (EIP) of mosquito-borne pathogens.ResultsAnopheles, Culex and Culiseta preferred artificial resting sites, while Aedes were predominantly collect in natural resting sites. Around 90% of the mosquitoes were collected from resting sites below 2 m. The mosquito species composition did not differ significantly between forest and meadow sites. Mean resting site temperatures near the ground were approximately 0.8 °C lower than at a height of 4–6 m, which changed the predicted mean EIP up to 5 days at meadow and 2 days at forest sites. Compared with temperature data from standardized weather stations, the resting site temperatures near the ground would prolong the mean estimated EIP 4 days at forest sites and 2 days at meadow sites.ConclusionsThe microclimate of mosquito resting sites differs from standardized meteorological data, which can influence the transmission of mosquito-borne pathogens. In a near-natural environment, colder temperatures at mosquitoes’ preferred resting sites near the ground would prolong the EIP of mosquito-borne pathogens relative to data from weather stations.Graphical

Brief communication: Western Europe flood in 2021 – mapping agriculture flood exposure from synthetic aperture radar (SAR)

Abstract. In this communication, we present the exposure of agricultural lands to the flooding caused by extreme precipitation in western Europe from 12 to 15 July 2021. Overlaying the flood inundation maps derived from the near-real-time RAdar-Produced Inundation Diary (RAPID) system on the Coordination of information on the environment (CORINE) Land Cover map we estimate a 1920 km2 area affected by the flooding, with 64 % representing agricultural land. Among the inundated agricultural land, 36 % of the area is pastures while 34 % is arable land. Most agricultural flood exposure is found in eastern France along the Rhône River, the southern Netherlands along the Meuse River, and western Germany along the Rhine River.

Modelling Hourly Particulate Matter (PM10) Concentrations at High Spatial Resolution in Germany Using Land Use Regression and Open Data

Air pollution is a major health risk factor worldwide. Regular short- and long-time exposures to ambient particulate matter (PM) promote various diseases and can lead to premature death. Therefore, in Germany, air quality is assessed continuously at approximately 400 measurement sites. However, knowledge about this intermediate distribution is either unknown or lacks a high spatial–temporal resolution to accurately determine exposure since commonly used chemical transport models are resource intensive. In this study, we present a method that can provide information about the ambient PM concentration for all of Germany at high spatial (100 m × 100 m) and hourly resolutions based on freely available data. To do so we adopted and optimised a method that combined land use regression modelling with a geostatistical interpolation technique using ordinary kriging. The land use regression model was set up based on CORINE (Coordination of Information on the Environment) land cover data and the Germany National Emission Inventory. To test the model’s performance under different conditions, four distinct data sets were used. (1) From a total of 8760 (365 × 24) available h, 1500 were randomly selected. From those, the hourly mean concentrations at all stations (ca. 400) were used to run the model (n = 566,326). The leave-one-out cross-validation resulted in a mean absolute error (MAE) of 7.68μgm−3 and a root mean square error (RMSE) of 11.20μgm−3. (2) For a more detailed analysis of how the model performs when an above-average number of high values are modelled, we selected all hourly means from February 2011 (n = 256,606). In February, measured concentrations were much higher than in any other month, leading to a slightly higher MAE of 9.77μgm−3 and RMSE of 14.36μgm−3, respectively. (3) To enable better comparability with other studies, the annual mean concentration (n = 413) was modelled with a MAE of 4.82μgm−3 and a RMSE of 6.08μgm−3. (4) To verify the model’s capability of predicting the exceedance of the daily mean limit value, daily means were modelled for all days in February (n = 10,845). The exceedances of the daily mean limit value of 50 μgm−3 were predicted correctly in 88.67% of all cases. We show that modelling ambient PM concentrations can be performed at a high spatial–temporal resolution for large areas based on open data, land use regression modelling, and kriging, with overall convincing results. This approach offers new possibilities in the fields of exposure assessment, city planning, and governance since it allows more accurate views of ambient PM concentrations at the spatial–temporal resolution required for such assessments.

Land cover changes in coffee cultural landscapes of Pereira (Colombia) between 1997 and 2014

Understanding how and what land cover changes and transitions have occurred in a territory is crucial to planning and managing high-demand surfaces. At the landscape level, the challenge is determining the allocation and management of various land cover options. Therefore, for natural resources planning and management, a study characterizing and analysing the territory of interest should be included. This work aimed to analyse the changes and land cover patterns in the city of Pereira, Colombia, within the framework of the Colombian Coffee Cultural Landscape. The evaluated period was between 1997 and 2014, and a Geographic Information System, ENVI 4.8 programme and QGIS programme were used for multitemporal analysis. To describe the land cover transitions, two temporal moments were analysed with Landsat satellite images: one moment was for the year 1997, which was taken in August (Landsat 5), and the other moment was for the year 2014, which was taken in July (Landsat 8). At level 1 of CORINE (Coordination of information on the environment), the areas of land cover corresponding to agricultural areas, forests and semi-natural areas decreased most in the analysis period, while artificial surfaces increased. At level 3, the cover with the greatest decrease in territory was coffee crops, which showed a negative annual loss rate of -3.97%, followed by permanent crops (-2.67%). The continuous and discontinuous urban fabric showed the greatest growth with a positive annual rate of 4.14%. In conclusion, the land cover that lost the most territory was coffee crop, mainly due to political-economic factors, such as the dissolution of the International Coffee Agreement and the National Federation of Coffee Growers that discouraged coffee cultivation and permanent crops. Likewise, sociocultural factors, such as smallholder farmers have guided the changes in land cover and have stimulated productive styles to adapt and remain, increasing heterogeneous agricultural areas.

Study of soil erosion risk in the basin of Northern Al-Kabeer river at Lattakia-Syria using remote sensing and GIS techniques

Soil erosion is the most importantchallenge foragriculturepresently onthe Syrian coast, especially in areas close to rivers and lakes.This study is to produce a soil erosion risk map based on the COoRdination of Information on the Environment (CORINE) model for the Northern Al-Kabeer River, Lattakia Province.To achieve this objective, the first phase of the study evaluated the soil erosion potential, by estimating soil texture, soil depth and granulometry. Soil was classified according to its erosion potential. A soil erosion potential map was obtained from data on soil erodibility, erosivity and slope.A land cover map of the study area was produced and classified into two classes according to soil protection. A risk map of soil erosion was prepared from information on land cover and potential risk erosion classes obtained previously.The results showed that 2.47% of the study area is in high risk soil erosion areas, 22.18% in moderate risk areas, and 75.35% in low risk areas. The high risk erosion areas were mainly located in the center and northern location within the study area. This study also confirms that land cover is the highest determinant of soil erosion.Land cover accounted for 60.93% of the erosion risk.

A new high resolution object-oriented approach to define the spatiotemporal dynamics of the cover-management factor in soil erosion modelling

The cover management factor (C-factor) calculation requires the assessment of the intra‐annual spatiotemporal variability of biomass cover, owed to the natural growth cycle of vegetation and the impact of agriculture on land cover. However, this is frequently omitted, and the vegetation conditions are approximated by assigning constant values to static classified Land Use/Land Cover (LULC) maps, such as the Coordination of Information on the Environment (CORINE) Land Cover (CLC). Using as test site the Sperchios River catchment, Central Greece, this study introduces a new approach to estimate C-factor in a spatiotemporally exhaustive manner. The goal is to increase estimation accuracy in erosion modelling applications. The C-factor computations are performed on monthly scale, based on LULC maps that portray the basin’s agricultural areas in unprecedented detail. The methodology involves the use of a biophysical index, namely Fraction of Vegetation Cover (Fcover) and empirical literature data on crop types. Fcover was developed from Sentinel-2 (S2) imagery in 10-m analysis. Such analysis (compared to the 300-m one provided by the EU) is a major improvement towards a more precise estimation of C-factor. The study identified the monthly C-factor fluctuation at basin scale, and the most susceptible months seasons at localities in terms of land cover/soil loss potential. The higher C-factor values were acquired in October and the lower in May. Mean annual (numerical) C-factor complies with the value of July. All monthly values are significantly higher – almost double – than the mean annual stationary one. The revealed patterns would not have been detected in a lower temporal (e.g., annual) resolution without the incorporation of vegetation density seasonality. The study shows high reproducibility and upscaling potential, as the utilized datasets are available in all European Union (EU) Member States, having similar structure, thus they can be harmonized towards a unified continental approach.

Pixel and object-based land cover mapping and change detection from 1986 to 2020 for Hungary using histogram-based gradient boosting classification tree classifier

The large-scale pixel-based land use/land cover classification is a challenging task, which depends on many circumstances. This study aims to create LULC maps with the nomenclature of Coordination of Information on the Environment (CORINE) Land Cover (CLC) for years when the CLC databases are not available. Furthermore, testing the predicted maps for land use changes in the last 30 years in Hungary. Histogram-based gradient boosting classification tree (HGBCT) classifier was tested at classification. According to the results, the classifier, with the use of texture variance and landscape metrics is capable to generate accurate predicted maps, and the comparison of the predicted maps provides a detailed image of the land use changes.

Risk assessment of soil erosion by using CORINE model in the western part of Syrian Arab Republic

BackgroundSoil erosion is a major threat to the natural ecosystem and agricultural sector in the western part of Lattakia Governorate, Syrian Arab Republic. The main goals of this research are to investigate erosion risk by using the Coordination of Information on the Environment (CORINE) Model and to prioritize areas for conservation practices. To achieve these goals, soil samples were collected from the field, the climatic data (i.e., rainfall) and Digital Elevation Model (DEM) were obtained and utilized to perform CORINE model in Geographic Information System (GIS) environment.ResultsThe results showed that only 13.2% of the study area was classified as high erodible. In addition, 45.24%, 49.15% and 5.29% of the study area were under low, moderate and high actual erosion risk, respectively. This research identified slope and land use/land cover as key factors responsible for soil erosion in the study area.ConclusionsThe CORINE model acknowledged as a good tool for predicting soil erosion and highlighting the areas affected by soil erosion in the study area with high precision.

Evaluation of Two Vegetation Indices (NDVI and VCI) Over Asi Basin in Turkey

Climate change and global warming are among the issues that humanity is most concerned about the future. The growing drought and flood risks that increase despite the taken measures have led to the adoption of an integrated understanding on the topic of water management in recent years. To manage the increased risk of drought and to make sustainable planning, the dimensions of drought should be known first. For this purpose, many drought indices have been developed. The Normalized Difference Vegetation Index (NDVI) and Vegetation Condition Index (VCI), which determined by remote sensing, are two of these. In this study, in which the agricultural drought was analyzed with vegetation indices by taking into consideration the historical drought archive, the Asi Basin was addressed. The data of the Asi Basin, which covers an area of 7800 km2 and was obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High-Resolution Radiometer (AVHRR) satellites, was used in this study. With the satellites benefited in remote sensing and with the Coordination of Information on the Environment (CORINE), where the layers of vegetation were determined, agricultural and forest areas were evaluated separately. The vegetation indices, which change with the increase in temperature, have revealed the necessity of a long-term drought management for the Asi Basin. Result of the work pointed that NDVI index is more appropriated to the Asi Basin than the VCI index to monitor drought.

Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (<1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86% vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.

Effect of Using Multi-Year Land Use Land Cover and Monthly LAI Inputs on the Calibration of a Distributed Hydrologic Model

Effective management of water resources entails the understanding of spatiotemporal changes in hydrologic fluxes with variation in land use, especially with a growing trend of urbanization, agricultural lands and non-stationarity of climate. This study explores the use of satellite-based Land Use Land Cover (LULC) data while simultaneously correcting potential evapotranspiration (PET) input with Leaf Area Index (LAI) to increase the performance of a physically distributed hydrologic model. The mesoscale hydrologic model (mHM) was selected for this purpose due to its unique features. Since LAI input informs the model about vegetation dynamics, we incorporated the LAI based PET correction option together with multi-year LULC data. The Globcover land cover data was selected for the single land cover cases, and hybrid of CORINE (coordination of information on the environment) and MODIS (Moderate Resolution Imaging Spectroradiometer) land cover datasets were chosen for the cases with multiple land cover datasets. These two datasets complement each other since MODIS has no separate forest class but more frequent (yearly) observations than CORINE. Calibration period spans from 1990 to 2006 and corresponding NSE (Nash-Sutcliffe Efficiency) values varies between 0.23 and 0.42, while the validation period spans from 2007 to 2010 and corresponding NSE values are between 0.13 and 0.39. The results revealed that the best performance is obtained when multiple land cover datasets are provided to the model and LAI data is used to correct PET, instead of default aspect-based PET correction in mHM. This study suggests that to minimize errors due to parameter uncertainties in physically distributed hydrologic models, adequate information can be supplied to the model with care taken to avoid over-parameterizing the model.

CORINE Veritabanı Üzerinden Türkiye’deki Madenlerin Yerleşimlere Yakınlıklarının Tespiti

The proximity of mineral extraction sites to settlements is a conflicting issue for urban studies. This study aims at exploring the change in the proximity of mines to settlements in Turkey between the years 2012 and 2018 and determining the cities which continue to be near mineral extraction sites. For this aim, analysis tools of Geographic Information Systems (GIS) are used with the mineral extraction sites and urban settlements layers of the Coordination of Information on the Environment (CORINE) land cover data of Turkey for these years. The findings of this study showed that the area of mines adjacent to settlements is increased from nearly 19905 hectares to nearly 21305 hectares and the area of mineral extraction sites within 1 and 500 meters to the nearest settlements is increased from nearly 16421 hectares to nearly 19967 hectares from 2012 to 2018.

The Land-Use Change Dynamics Based on the CORINE Data in the Period 1990–2018 in the European Archipelagos of the Macaronesia Region: Azores, Canary Islands, and Madeira

Islands as peripheral and ultra-peripheral are typically highlighted as ecologically sensitive areas to human activities due to the tremendous biological diversity of beings and the future possibility of habitat loss. In this regard, the comprehension of the land occupation dynamics and trends in the ultra-peripheral territories is crucial to attempt long-lasting regional sustainability, as is the island region’s case. Therefore, the present article aims to analyze the trends and dynamics of the land-use changes on the European Archipelagos of the Macaronesia Region over the last three decades, using the CORINE (Coordination of Information on the Environment) data. Some of the obtained results show that about 3.4% of the Azores’ surface is characterized mainly by discontinuous urban fabric, representing 67% of the total urban fabric of the Azores over the last thirty years. Additionally, in Madeira Archipelago, the land is mainly occupied by forest and semi-natural areas, representing almost three-thirds of the territory. A similar scenario is verified in the Canary Islands, where forests and semi-natural areas represent approximately three-quarters of the territory. Once more, this study shows the relevance of the island areas’ unique character, which should be preserved and protected. Therefore, the priorities must be defined and established management strategies that are significant for the well-being of these highly valued areas. Moreover, the study showed that notable changes had occurred in the period 1990–2018 in this landscape. Hence there is a need for appropriate measures to mitigate these negative impacts on the environment.

Land Cover of Early-Life Environment Modulates the Risk of Type 1 Diabetes.

OBJECTIVEEnvironmental microbial exposures have been implicated to protect against immune-mediated diseases such as type 1 diabetes. Our objective was to study the association of land cover around the early-life dwelling with the development of islet autoimmunity and type 1 diabetes to evaluate the role of environmental microbial biodiversity in the pathogenesis.RESEARCH DESIGN AND METHODSAssociation between land cover types and the future risk of type 1 diabetes was studied by analyzing land cover types classified according to Coordination of Information on the Environment (CORINE) 2012 and 2000 data around the dwelling during the first year of life for 10,681 children genotyped for disease-associated HLA-DQ alleles and monitored from birth in the Type 1 Diabetes Prediction and Prevention (DIPP) study. Land cover was compared between children who developed type 1 diabetes (n = 271) or multiple diabetes-associated islet autoantibodies (n = 384) and children without diabetes who are negative for diabetes autoantibodies.RESULTSAgricultural land cover around the home was inversely associated with diabetes risk (odds ratio 0.37, 95% CI 0.16–0.87, P = 0.02 within a distance of 1,500 m). The association was observed among children with the high-risk HLA genotype and among those living in the southernmost study region. Snow cover on the ground seemed to block the transfer of the microbial community indoors, leading to reduced bacterial richness and diversity indoors, which might explain the regional difference in the association. In survival models, an agricultural environment was associated with a decreased risk of multiple islet autoantibodies (hazard ratio [HR] 1.60, P = 0.008) and a decreased risk of progression from single to multiple autoantibody positivity (HR 2.07, P = 0.001) compared with an urban environment known to have lower environmental microbial diversity.CONCLUSIONSThe study suggests that exposure to an agricultural environment (comprising nonirrigated arable land, fruit trees and berry plantations, pastures, natural pastures, land principally occupied by agriculture with significant areas of natural vegetation, and agroforestry areas) early in life is inversely associated with the risk of type 1 diabetes. This association may be mediated by early exposure to environmental microbial diversity.

Land Use Changes in Turkish Territories: Patterns, Directions and Socio-Economic Impacts on Territorial Management

Human activities have been changing the Earth's cover at an unparalleled scale. In this regard, and cover mapping is a decisive advantage for several kinds of research. Also, the outcomes from these investigations could be applied to plan a sustainable regional governance policy. This article studied land-use changes in the Turkish Territories in 1990, 2000, 2012, and 2018 using the Coordination of Information on the Environment (CORINE) data. The results showed a significant and gradual land-use change from agricultural to mostly artificial surfaces. The majority of land-use changes are related to industrial and commercial units and construction sites. The most degraded agricultural land uses are non-irrigated arable land and pastures, while there is an increasing trend in permanently irrigated land. This study's outcome can be considered a surveying baseline for the comparative analysis of similar works for different land-use change trends in Europe or worldwide. Landuse change studies are reliable tools to evaluate the human activities and footprint of proposed strategies and policies in a territory. This article also enables us to understand that Turkey's decisive actors should design development policies to encourage industrial investments and agricultural ventures in Turkey and adapt the land-use/land cover strategies to mitigate agricultural land fragmentation.

Exploring Short-Term Climate Change Effects on Rangelands and Broad-Leaved Forests by Free Satellite Data in Aosta Valley (Northwest Italy)

Satellite remote sensing is a power tool for the long-term monitoring of vegetation. This work, with reference to a regional case study, investigates remote sensing potentialities for describing the annual phenology of rangelands and broad-leaved forests at the landscape level with the aim of detecting eventual effects of climate change in the Alpine region of the Aosta Valley (Northwest (NW) Italy). A first analysis was aimed at estimating phenological metrics (PMs) from satellite images time series and testing the presence of trends along time. A further investigation concerned evapotranspiration from vegetation (ET) and its variation along the years. Additionally, in both the cases the following meteorological patterns were considered: air temperature anomalies, precipitation trends and the timing of yearly seasonal snow melt. The analysis was based on the time series (TS) of different MODIS collections datasets together with Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) collection obtained through Google Earth Engine. Ground weather stations data from the Centro Funzionale VdA ranging from 2000 to 2019 were used. In particular, the MOD13Q1 v.6, MOD16A2 and MOD10A1 v.6 collections were used to derive PMs, ET and snow cover maps. The SRTM (shuttle radar topography mission) DTM (digital terrain model) was also used to describe local topography while the Coordination of Information on the Environment (CORINE) land cover map was adopted to investigate land use classes. Averagely in the area, rangelands and broad-leaved forests showed that the length of season is getting longer, with a general advance of the SOS (start of the season) and a delay in the EOS (end of the season). With reference to ET, significant increasing trends were generally observed. The water requirement from vegetation appeared to have averagely risen about 0.05 Kg·m−2 (about 0.5%) per year in the period 2000–2019, for a total increase of about 1 Kg·m−2 in 20 years (corresponding to a percentage difference in water requirement from vegetation of about 8%). This aspect can be particularly relevant in the bottom of the central valley, where the precipitations have shown a statistically significant decreasing trend in the period 2000–2019 (conversely, no significant variation was found in the whole territory). Additionally, the snowpack timing persistence showed a general reduction trend. PMs and ET and air temperature anomalies, as well as snow cover melting, proved to have significantly changed their values in the last 20 years, with a continuous progressive trend. The results encourage the adoption of remote sensing to monitor climate change effects on alpine vegetation, with particular focus on the relationship between phenology and other abiotic factors permitting an effective technological transfer.

Monitoring of land subsidence due to excessive groundwater extraction using small baseline subset technique in Konya, Turkey.

Konya, which is located within the Konya Closed Basin, is the most important agricultural production region in Turkey. The future of agriculture is threatened in this region due to the decline in groundwater levels and the intensive agricultural activities that require high water consumption and are not suitable to the climate conditions of the region. In addition to these parameters, the geological structure of Konya also poses various environmental problems such as land subsidence and sinkhole formation. This study aimed to investigate the causes of the land subsidence problem in Konya and its surroundings with the help of the interferometric synthetic aperture radar (InSAR) technique and auxiliary data, namely optic, Coordination of Information on the Environment (CORINE), and groundwater monitoring station data. In order to investigate the land subsidence in the study area, 58 Sentinel-1A images acquired between 2014 and 2018 were processed by using the small baseline subset (SBAS) technique. In addition, the time series derived from the SAR data was validated by a global navigation satellite system (GNSS) station located in the study area. The results revealed that severe land subsidence, some of which reached 75 mm/year, occurred in certain areas of the study area over a period of three and a half years. High consistency was found between the land subsidence and the groundwater level change observed in the region, with a cross-correlation of over 95%. Moreover, the temporal and spatial patterns of the cultivated area and urbanization, which are the main reasons for the consumption of groundwater in the region, were revealed using the optic data.