•We included spatially-varying relationships in our Europe-wide models.•We built spatially- and temporally-varying models to estimate annual air pollution.•Spatially-varying linear regression was ...more robust than a machine learning method.
Previous European land-use regression (LUR) models assumed fixed linear relationships between air pollution concentrations and predictors such as traffic and land use. We evaluated whether including spatially-varying relationships could improve European LUR models by using geographically weighted regression (GWR) and random forest (RF). We built separate LUR models for each year from 2000 to 2019 for NO2, O3, PM2.5 and PM10 using annual average monitoring observations across Europe. Potential predictors included satellite retrievals, chemical transport model estimates and land-use variables. Supervised linear regression (SLR) was used to select predictors, and then GWR estimated the potentially spatially-varying coefficients. We developed multi-year models using geographically and temporally weighted regression (GTWR). Five-fold cross-validation per year showed that GWR and GTWR explained similar spatial variations in annual average concentrations (average R2 = NO2: 0.66; O3: 0.58; PM10: 0.62; PM2.5: 0.77), which are better than SLR (average R2 = NO2: 0.61; O3: 0.46; PM10: 0.51; PM2.5: 0.75) and RF (average R2 = NO2: 0.64; O3: 0.53; PM10: 0.56; PM2.5: 0.67). The GTWR predictions and a previously-used method of back-extrapolating 2010 model predictions using CTM were overall highly correlated (R2 > 0.8) for all pollutants. Including spatially-varying relationships using GWR modestly improved European air pollution annual LUR models, allowing time-varying exposure-health risk models.
•Mangrove dynamics were analysed using satellite images between 2000 and 2018.•The coast of Suriname is characterized by mangrove gain and loss linked to migrating mud banks.•Mangrove expansion is ...fast; NDVI-values increase from 0 to 0.7 within 3 years.•Satellite images provide insight into various spatial patterns of mangrove colonization.
Mangroves play an important role in protecting coasts against wave energy and storms. Mangrove ecosystems provide important habitats for fauna and flora and are an important carbon sink. Loss of mangroves forest may lead to enhanced coastal erosion. Mangroves are complex ecosystems and processes of settling and development are not fully understood. Characterizing the rates and patterns of mangrove gains and losses is needed to better understand the functioning of mangrove ecosystems, how mangrove dynamics are linked to coastal morphological behaviour and how human interference with the coastal system impacts mangroves. Here we present a study of the mangrove ecosystems at the Suriname coast, which are relatively pristine and characterized by strong dynamics due to migrating mudbanks along the coast. Satellite images between 2000 and 2018, available in the historic satellite image archives, were analysed using the LandTrendr (Landsat-based detection of trends in disturbance and recovery) algorithm to identify locations of mangrove erosion, mangrove colonization, surface areas of change and patterns of settlement, as indicated by (sudden) changes in NDVI. The algorithm requires careful setting of various parameters for successful detection of (abrupt) temporal changes in mangrove coverage. The algorithm was evaluated on its robustness using various parameter settings. Results show the value of the timeseries of Landsat imagery to detect locations of coastal erosion of up to 50 m/yr and accretion where loss or settlement of mangroves is prevailing between 2000 and 2018. Locally differences are very large. An overall westward mangrove progression along the coast is apparent from the images and probably linked to mud bank migration. Various patterns of mangrove colonization and development such as arc-, zonal- and patch- arrangements were identified, although at some locations the Landsat resolution of 30 m is somewhat coarse to allow detailed analysis. The success and robustness of the LandTrendr algorithm are controlled by NDVI threshold values, number of allowed breakpoints in the timeseries and fitting parameters. The presented method requires further testing and evaluation but is a promising tool for semi-automatic detection of coastal mangrove erosion and colonization that can be applied to other mangrove ecosystems in the world. The satellite timeseries analyses generate valuable information on coastal dynamics, which is helpful to identify coastal areas prone to erosion and mangrove retreat and provide as such a valuable tool for coastal management and protection.
•Little is known about the link between air pollution and pediatric epilepsy.•We evaluated the association between four ambient air pollutants and the diagnosis of epilepsy in children.•No ...association between ambient air pollution and an epilepsy diagnosis was observed.
Increasing evidence suggests that exposure to air pollution is linked to neurological disorders, but little is known about the association with epilepsy. This study aimed to quantify the association between exposure to ambient air pollutants and the diagnosis of epilepsy in Dutch children. A population-based case-control study was conducted among children presenting to the first seizure clinic at the Wilhelmina Children's Hospital in Utrecht, the Netherlands, from 1 January 2008 to 31 May 2021. Children were assigned to either cases (i.e., diagnosed with epilepsy, n = 406) or controls (n = 737). Levels of ambient air pollution (nitrogen dioxide NO2, ozone O3, and particulate matter with aerodynamic diameter < 10 μm PM10 and < 2.5 μm PM2.5) exposure were assigned for the year of presentation to the residential addresses of study participants using EU-wide air pollution metrics. Logistic regression models, adjusted for common confounders, were applied to calculate odds ratios (ORs) with 95 % confidence intervals (CIs) for the association between air pollution and epilepsy. Overall, no association between ambient air pollution and an epilepsy diagnosis was observed, including NO2 (OR: 1.01, 95 % CI: 0.98, 1.03), O3 (OR: 1.01, 95 % CI: 0.98, 1.03), PM2.5 (OR: 0.99, 95 % CI: 0.94, 1.04), and PM10 (OR: 0.99, 95 % CI: 0.95, 1.02). Subgroup analysis was suggestive but ultimately underpowered to draw any meaningful conclusions. Additional work, including a longitudinal evaluation of air pollutants, a closer examination of epilepsy etiologies, and a wider, community-based approach, is needed to explore these findings further.
Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in ...estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO2) concentrations for each hour of the day. Using mobile NO2 data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R2 of 0.49 and a Mean Absolute Error of 6.33 μg/m3, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.
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•Mobile monitoring can measure fine spatiotemporal air pollution concentrations.•Rare studies focus on producing temporally varying maps using mobile measurements.•Mobile data follow the pattern of routine monitors, but with significant deviations.•GTWR can efficiently map long-term average hourly air pollution concentrations.•Hourly maps facilitate dynamic exposure assessment based on human activities.
To improve streamflow predictions, researchers have implemented updating procedures that correct predictions from a simulation model using machine learning methods, in which simulated streamflow and ...meteorological data are used as predictors. Few studies however have included an extensive set of meteorological and hydrological state variables simulated by the simulation model. We developed and evaluated a Random Forests (RF)-based approach to correct predictions from a global hydrological model PCR-GLOBWB. From PCR-GLOBWB, meteorological input as well as its simulated hydrological state variables were used as predictors in the RF to estimate errors of PCR-GLOBWB streamflow predictions, which were then applied to correct simulated hydrograph. The RF was trained and applied separately at three streamflow gauging stations in the Rhine basin with different physiographic characteristics. Daily streamflow simulations from an uncalibrated PCR-GLOBWB run were improved by applying the RF-based error-correction model (KGE improved from 0.37 to 0.62 to 0.76–0.89, NSE from 0.19 to 0.39 to 0.64–0.80). A similar improvement was found in the simulations from a calibrated PCR-GLOBWB run (KGE 0.72–0.87 and NSE 0.60–0.78). The PCR-GLOBWB state variables that are informative to the improvement differed between catchments. Variables related to groundwater are informative in catchments dominated by the sedimentary basins characterizing large aquifers, while snow cover and surface water state variables are informative in a nival regime with large lakes. Here we quantified the improvement from combining a process-based and machine learning approach.
•A random forest for correcting daily discharge predictions is proposed.•Meteorological and simulated state variables are involved in the random forest.•The most important variables vary with catchments with different characteristics.•The model framework has shown to address the temporal autocorrelation.
Mobile air quality measurements are collected typically for several seconds per road segment and in specific timeslots (e.g., working hours). These short-term and on-road characteristics of mobile ...measurements become the ubiquitous shortcomings of applying land use regression (LUR) models to estimate long-term concentrations at residential addresses. This issue was previously found to be mitigated by transferring LUR models to the long-term residential domain using routine long-term measurements in the studied region as the transfer target (local scale). However, long-term measurements are generally sparse in individual cities. For this scenario, we propose an alternative by taking long-term measurements collected over a larger geographical area (global scale) as the transfer target and local mobile measurements as the source (Global2Local model). We empirically tested national, airshed countries (i.e., national plus neighboring countries) and Europe as the global scale in developing Global2Local models to map nitrogen dioxide (NO2) concentrations in Amsterdam. The airshed countries scale provided the lowest absolute errors, and the Europe-wide scale had the highest R2. Compared to a “global” LUR model (trained exclusively with European-wide long-term measurements), and a local mobile LUR model (using mobile data from Amsterdam only), the Global2Local model significantly reduced the absolute error of the local mobile LUR model (root-mean-square error, 6.9 vs 12.6 μg/m3) and improved the percentage explained variances compared to the global model (R2, 0.43 vs 0.28, assessed by independent long-term NO2 measurements in Amsterdam, n = 90). The Global2Local method improves the generalizability of mobile measurements in mapping long-term residential concentrations with a fine spatial resolution, which is preferred in environmental epidemiological studies.
•Short-term mobile data hinder LUR models in mapping long-term residential NO2.•The Global2Local model can mitigate this issue by using transfer learning techniques.•It can integrate local mobile and global stationary measurements.•It outperformed LUR models using measurements from the global or local scale only.•It is widely applicable to all regions with mobile monitoring data.
Mobile monitoring campaigns have effectively captured spatial hyperlocal variations in long-term average concentrations of regulated and unregulated air pollutants. However, their application in ...estimating spatiotemporally varying maps has rarely been investigated. Tackling this gap, we investigated whether mobile measurements can assess long-term average nitrogen dioxide (NO
) concentrations for each hour of the day. Using mobile NO
data monitored for 10 months in Amsterdam, we examined the performance of two spatiotemporal land use regression (LUR) methods, Spatiotemporal-Kriging and GTWR (Geographical and Temporal Weighted Regression), alongside two classical spatial LUR models developed separately for each hour. We found that mobile measurements follow the general pattern of fixed-site measurements, but with considerable deviations (indicating collection uncertainty). Leveraging heterogeneous spatiotemporal autocorrelations, GTWR smoothed these deviations and achieved an overall performance of an R
of 0.49 and a Mean Absolute Error of 6.33 μg/m
, validated by long-term fixed-site measurements (out-of-sample). The other models tested were more affected by the collection uncertainty. We highlighted that the spatiotemporal variations captured in mobile measurements can be used to reconstruct long-term average hourly air pollution maps. These maps facilitate dynamic exposure assessments considering spatiotemporal human activity patterns.
Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and ...consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO2 and PM2.5, for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016–2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO2 models performed overall well (5-fold cross validation R2 = 0.50–0.78), while the PM2.5 performed moderately (5-fold cross validation R2 = 0.24–0.62). Both for NO2 and PM2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays’. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO2 hourly R2 = 0.35–0.70; PM2.5 hourly R2 = 0.01–0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data.
•Hourly LUR models for NO2 and PM2.5 are developed with SLR and RF.•Models' performance and structure differ between hours, seasons and weekday type.•Hourly LUR models outperformed temporal adjustment of annual surfaces.
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