Found 818 publications. Showing page 33 of 35:
Marine mammals are considered sentinel species and may act as indicators of ocean health. Plastic residues are widely distributed in the oceans and are recognised as hazardous contaminants, and once ingested can cause several adverse effects on wildlife. This study aimed to identify and characterise plastic ingestion in the Guiana dolphins (Sotalia guianensis) from the Southwestern Tropical Atlantic by evaluating the stomach contents of stranded individuals through KOH digestion and identification of subsample of particles by LDIR Chemical Imaging System. Most of the individuals were contaminated, and the most common polymers identified were PU, PET and EVA. Microplastics were more prevalent than larger plastic particles (meso- and macroplastics). Smaller particles were detected during the rainy seasons. Moreover, there was a positive correlation between the stomach content mass and the number of microplastics, suggesting contamination through trophic transfer.
2023
Global Historical Stocks and Emissions of PBDEs
The first spatially and temporally resolved inventory of BDE28, 47, 99, 153, 183, and 209 in the anthroposphere and environment is presented here. The stock and emissions of PBDE congeners were estimated using a dynamic substance flow analysis model, CiP-CAFE. To evaluate our results, the emission estimates were used as input to the BETR-Global model. Estimated concentrations were compared with observed concentrations in air from background areas. The global (a) in-use and (b) waste stocks of ∑5BDE(28, 47, 99, 153, 183) and BDE209 are estimated to be (a) ∼25 and 400 kt and (b) 13 and 100 kt, respectively, in 2018. A total of 6 (0.3–13) and 10.5 (9–12) kt of ∑5BDE and BDE209, respectively, has been emitted to the atmosphere by 2018. More than 70% of PBDE emissions during production and use occurred in the industrialized regions, while more than 70% of the emissions during waste disposal occurred in the less industrialized regions. A total of 70 kt of ∑5BDE and BDE209 was recycled within products since 1970. As recycling rates are expected to increase under the circular economy, an additional 45 kt of PBDEs (mainly BDE209) may reappear in new products.
2019
In order to evaluate the potential impact of the Arctic anthropogenic emission sources it is essential to understand better the natural aerosol sources of the inner Arctic and the atmospheric processing of the aerosols during their transport in the Arctic atmosphere. A 1-year time series of chemically specific measurements of the sub-micrometre aerosol during 2015 has been taken at the Mt. Zeppelin observatory in the European Arctic. A source apportionment study combined measured molecular tracers as source markers, positive matrix factorization, analysis of the potential source distribution and auxiliary information from satellite data and ground-based observations. The annual average sub-micrometre mass was apportioned to regional background secondary sulphate (56%), sea spray (17%), biomass burning (15%), secondary nitrate (5.8%), secondary marine biogenic (4.5%), mixed combustion (1.6%), and two types of marine gel sources (together 0.7%). Secondary nitrate aerosol mainly contributed towards the end of summer and during autumn. During spring and summer, the secondary marine biogenic factor reached a contribution of up to 50% in some samples. The most likely origin of the mixed combustion source is due to oil and gas extraction activities in Eastern Siberia. The two marine polymer gel sources predominantly occurred in autumn and winter. The small contribution of the marine gel sources at Mt. Zeppelin observatory in summer as opposed to regions closer to the North Pole is attributed to differences in ocean biology, vertical distribution of phytoplankton, and the earlier start of the summer season.
2019
Curating scientific information in knowledge infrastructures
Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information—meaningful secondary or derived data—about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation—as machine readable data and their machine readable meaning—is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.
2018
In the present study, concentrations of legacy and emerging contaminants were determined in three non-destructive matrices (plasma, preen oil and body feathers) of northern goshawk (Accipiter gentilis) nestlings. Persistent organic pollutants (POPs), including polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs) and polybrominated diphenyl ethers (PBDEs), together with emerging pollutants, including per- and polyfluorinated alkyl substances (PFASs), novel brominated flame retardants (NBFRs), phosphorus flame retardants (PFRs) and Dechlorane Plus isomers (DPs) were targeted. Plasma, preen oil and feather samples were collected from 61 goshawk nestlings in Norway (Trøndelag and Troms) in 2015 and 2016, and pollutant concentrations were compared between the three matrices. In plasma, PFASs were detected in the highest concentrations, ranging between 1.37 and 36.0 ng/mL, which suggests that the nestlings were recently and continuously exposed to these emerging contaminants, likely through dietary input. In preen oil, OCPs (169–3560 ng/g) showed the highest concentrations among the investigated compounds, consistent with their high lipophilicity. PFRs (2.60–314 ng/g) were the dominant compounds in feathers and are thought to originate mainly from external deposition, as they were not detected in the other two matrices. NBFRs and DPs were generally not detected in the nestlings, suggesting low presence of these emerging contaminants in their environment and/or low absorption. Strong and significant correlations between matrices were found for all POPs (rs = 0.46–0.95, p < 0.001), except for hexachlorobenzene (HCB, rs = 0.20, p = 0.13). Correlations for PFASs were less conclusive: linear perfluorooctane sulfonate (PFOS), perfluoroundecanoate (PFUnA), perfluorododecanoate (PFDoA) and perfluorotetradecanoate (PFTeA) showed strong and significant correlations between plasma and feathers (rs = 0.42–0.72, p < 0.02), however no correlation was found for perfluorohexane sulfonate (PFHxS), perfluorononanoate (PFNA) and perfluorotridecanoate (PFTriA) (rs = 0.05–0.33, p = 0.09–0.85). A lack of consistency between the PFAS compounds (contrary to POPs), and between studies, prevents concluding on the suitability of the investigated matrices for PFAS biomonitoring.
2019
Understanding aerosol–cloud–climate interactions in the Arctic is key to predicting the climate in this rapidly changing region. Whilst many studies have focused on submicrometer aerosol (diameter less than 1 µm), relatively little is known about the supermicrometer aerosol (diameter above 1 µm). Here, we present a cluster analysis of multiyear (2015–2019) aerodynamic volume size distributions, with diameter ranging from 0.5 to 20 µm, measured continuously at the Gruvebadet Observatory in the Svalbard archipelago. Together with aerosol chemical composition data from several online and offline measurements, we apportioned the occurrence of the coarse-mode aerosols during the study period (mainly from March to October) to anthropogenic (two sources, 27 %) and natural (three sources, 73 %) origins. Specifically, two clusters are related to Arctic haze with high levels of black carbon, sulfate and accumulation mode (0.1–1 µm) aerosol. The first cluster (9 %) is attributed to ammonium sulfate-rich Arctic haze particles, whereas the second one (18 %) is attributed to larger-mode aerosol mixed with sea salt. The three natural aerosol clusters were open-ocean sea spray aerosol (34 %), mineral dust (7 %) and an unidentified source of sea spray-related aerosol (32 %). The results suggest that sea-spray-related aerosol in polar regions may be more complex than previously thought due to short- and long-distance origins and mixtures with Arctic haze, biogenic and likely blowing snow aerosols. Studying supermicrometer natural aerosol in the Arctic is imperative for understanding the impacts of changing natural processes on Arctic aerosol.
2021
In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD – Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations.
The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.
2024
Polyurethane foam passive air samplers (PUF-PAS) are the most common type of passive air sampler used for a range of semi-volatile organic compounds (SVOCs), including regulated persistent organic pollutants (POPs) and polycyclic aromatic hydrocarbons (PAHs), and emerging contaminants (e.g., novel flame retardants, phthalates, current-use pesticides). Data from PUF-PAS are key indicators of effectiveness of global regulatory actions on SVOCs, such as the Global Monitoring Plan of the Stockholm Convention on Persistent Organic Pollutants. While most PUF-PAS use similar double-dome metal shielding, there is no standardized dome size, shape, or deployment configuration, with many different PUF-PAS designs used in regional and global monitoring. Yet, no information is available on the comparability of data from studies using different PUF-PAS designs. We brought together 12 types of PUF-PAS used by different research groups around the world and deployed them in a multi-part intercomparison to evaluate the variability in reported concentrations introduced by different elements of PAS monitoring. PUF-PAS were deployed for 3 months in outdoor air in Kjeller, Norway in 2015–2016 in three phases to capture (1) the influence of sampler design on data comparability, (2) the influence of analytical variability when samplers are analyzed at different laboratories, and (3) the overall variability in global monitoring data introduced by differences in sampler configurations and analytical methods. Results indicate that while differences in sampler design (in particular, the spacing between the upper and lower sampler bowls) account for up to 50 % differences in masses collected by samplers, the variability introduced by analysis in different laboratories far exceeds this amount, resulting in differences spanning orders of magnitude for POPs and PAHs. The high level of variability due to analysis in different laboratories indicates that current SVOC air sampling data (i.e., not just for PUF-PAS but likely also for active air sampling) are not directly comparable between laboratories/monitoring programs. To support on-going efforts to mobilize more SVOC data to contribute to effectiveness evaluation, intercalibration exercises to account for uncertainties in air sampling, repeated at regular intervals, must be established to ensure analytical comparability and avoid biases in global-scale assessments of SVOCs in air caused by differences in laboratory performance.
2021
Grouping strategies are needed for per- and polyfluoroalkyl substances (PFAS), in part, because it would be time and resource intensive to test and evaluate the more than 4700 PFAS on the global market on a chemical-by-chemical basis. In this paper we review various grouping strategies that could be used to inform actions on these chemicals and outline the motivations, advantages and disadvantages for each. Grouping strategies are subdivided into (1) those based on the intrinsic properties of the PFAS (e.g. persistence, bioaccumulation potential, toxicity, mobility, molecular size) and (2) those that inform risk assessment through estimation of cumulative exposure and/or effects. The most precautionary grouping approach of those reviewed within this article suggests phasing out PFAS based on their high persistence alone (the so-called “P-sufficient” approach). The least precautionary grouping approach reviewed advocates only grouping PFAS for risk assessment that have the same toxicological effects, modes and mechanisms of action, and elimination kinetics, which would need to be well documented across different PFAS. It is recognised that, given jurisdictional differences in chemical assessment philosophies and methodologies, no one strategy will be generally acceptable. The guiding question we apply to the reviewed grouping strategies is: grouping for what purpose? The motivation behind the grouping (e.g. determining use in products vs. setting guideline levels for contaminated environments) may lead to different grouping decisions. This assessment provides the necessary context for grouping strategies such that they can be adopted as they are, or built on further, to protect human and environmental health from potential PFAS-related effects.
2020
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2019
Atlantic multidecadal oscillation modulates the impacts of Arctic sea ice decline
The Arctic sea ice cover has been rapidly declining in the last two decades, concurrent with a shift in the Atlantic Multidecadal Oscillation (AMO) to its warm phase around 1996/1997. Here we use both observations and model simulations to investigate the modulation of the atmospheric impacts of the decreased sea ice cover in the Atlantic sector of the Arctic (AASIC) by the AMO. We find that the AASIC loss during a cold AMO phase induces increased Ural blocking activity, a southeastward‐extended snowpack, and a cold continent anomaly over Eurasia in December through northerly cold air advection and moisture transport from the Arctic. The increased Ural blocking activity and more extended Eurasian snowpack strengthen the upward propagation of planetary waves over the Siberian‐Pacific sector in the lower stratosphere and hence lead to a weakened stratospheric polar vortex and a negative Arctic Oscillation (AO) phase at the surface in February. However, corresponding to the AASIC loss during a warm AMO phase, one finds more widespread warming over the Arctic and a reduced snowpack over Northern Eurasia in December. The stratosphere‐troposphere coupling is suppressed in early winter and no negative AO anomaly is found in February. We suggest that the cold AMO phase is important to regulate the atmospheric response to AASIC decline, and our study provides insight to the ongoing debate on the connection between the Arctic sea ice and the AO.
2018
This paper presents the validation results of Aerosol Optical Depth (AOD) retrieved from the Spinning Enhanced Visible Infrared Radiometer (SEVIRI) data using the near-real-time algorithm further developed in the frame of the Satellite-based Monitoring Initiative for Regional Air quality (SAMIRA) project. The SEVIRI AOD was compared against multiple data sources: six stations of the Aerosol Robotic Network (AERONET) in Romania and Poland, three stations of the Aerosol Research Network in Poland (Poland–AOD) and Moderate Resolution Imaging Spectroradiometer (MODIS) data overlapping Romania, Czech Republic and Poland. The correlation values between a four-month dataset (June–September 2014) from SEVIRI and the closest temporally available data for both ground-based and satellite products were identified. The comparison of the SEVIRI AOD with the AERONET AOD observations generally shows a good correlation (r = 0.48–0.83). The mean bias is 0.10–0.14 and the root mean square error RMSE is between 0.11 and 0.15 for all six stations cases. For the comparison with Poland–AOD correlation values are 0.55 to 0.71. The mean bias is 0.04–0.13 and RMSE is between 0.10 and 0.14. As for the intercomparison to MODIS AOD, correlations values were generally lower (r = 0.33–0.39). Biases of −0.06 to 0.24 and RMSE of 0.04 to 0.28 were in good agreement with the ground–stations retrievals. The validation of SEVIRI AOD with AERONET results in the best correlations followed by the Poland–AOD network and MODIS retrievals. The average uncertainty estimates are evaluated resulting in most of the AOD values falling above the expected error range. A revised uncertainty estimate is proposed by including the observed bias form the AERONET validation efforts.
2021
Retrievals of trace gas concentrations from satellite observations are mostly performed for clear regions or regions with low cloud coverage. However, even fully clear pixels can be affected by clouds in the vicinity, either by shadowing or by scattering of radiation from clouds in the clear region. Quantifying the error of retrieved trace gas concentrations due to cloud scattering is a difficult task. One possibility is to generate synthetic data by three-dimensional (3D) radiative transfer simulations using realistic 3D atmospheric input data, including 3D cloud structures. Retrieval algorithms may be applied on the synthetic data, and comparison to the known input trace gas concentrations yields the retrieval error due to cloud scattering.
In this paper we present a comprehensive synthetic dataset which has been generated using the Monte Carlo radiative transfer model MYSTIC (Monte Carlo code for the phYSically correct Tracing of photons In Cloudy atmospheres). The dataset includes simulated spectra in two spectral ranges (400–500 nm and the O2A-band from 755–775 nm). Moreover it includes layer air mass factors (layer-AMFs) calculated at 460 nm. All simulations are performed for a fixed background atmosphere for various sun positions, viewing directions and surface albedos.
Two cloud setups are considered: the first includes simple box clouds with various geometrical and optical thicknesses. This can be used to systematically investigate the sensitivity of the retrieval error on solar zenith angle, surface albedo and cloud parameters. Corresponding 1D simulations are also provided. The second includes realistic three-dimensional clouds from an ICON large eddy simulation (LES) for a region covering Germany and parts of surrounding countries. The scene includes cloud types typical of central Europe such as shallow cumulus, convective cloud cells, cirrus and stratocumulus. This large dataset can be used to quantify the trace gas concentration retrieval error statistically.
Along with the dataset, the impact of horizontal photon transport on reflectance spectra and layer-AMFs is analysed for the box-cloud scenarios. Moreover, the impact of 3D cloud scattering on the NO2 vertical column density (VCD) retrieval is presented for a specific LES case. We find that the retrieval error is largest in cloud shadow regions, where the NO2 VCD is underestimated by more than 20 %.
The dataset is available for the scientific community to assess the behaviour of trace gas retrieval algorithms and cloud correction schemes in cloud conditions with 3D structure.
2022
Assessing, quantifying and valuing the ecosystem services of coastal lagoons
The natural conservation of coastal lagoons is important not only for their ecological importance, but also because of the valuable ecosystem services they provide for human welfare and wellbeing. Coastal lagoons are shallow semi-enclosed systems that support important habitats such as wetlands, mangroves, salt-marshes and seagrass meadows, as well as a rich biodiversity. Coastal lagoons are also complex social-ecological systems and the ecosystem services that lagoons deliver provide livelihoods, benefits wellbeing and welfare to humans. This study assessed, quantified and valued the ecosystem services of 32 coastal lagoons. The main findings of the study were: (i) the definitions of ecosystem services are still not generally accepted; (ii) the quantification of ecosystem services is made in many different ways, using different units; (iii) the evaluation in monetary terms of some ecosystem service is problematic, often relying on non-monetary evaluation methods; (iv) when ecosystem services are valued in monetary terms, this may represent very different human benefits; and, (v) different aspects of climate change, including increasing temperature (SST), sea-level rise (SLR) and changes in rainfall patterns threaten the valuable ecosystem services of coastal lagoons.
2018
Aerosol particles are essential constituents of the Earth's atmosphere, impacting the earth radiation balance directly by scattering and absorbing solar radiation, and indirectly by acting as cloud condensation nuclei. In contrast to most greenhouse gases, aerosol particles have short atmospheric residence times, resulting in a highly heterogeneous distribution in space and time. There is a clear need to document this variability at regional scale through observations involving, in particular, the in situ near-surface segment of the atmospheric observation system. This paper will provide the widest effort so far to document variability of climate-relevant in situ aerosol properties (namely wavelength dependent particle light scattering and absorption coefficients, particle number concentration and particle number size distribution) from all sites connected to the Global Atmosphere Watch network. High-quality data from almost 90 stations worldwide have been collected and controlled for quality and are reported for a reference year in 2017, providing a very extended and robust view of the variability of these variables worldwide. The range of variability observed worldwide for light scattering and absorption coefficients, single-scattering albedo, and particle number concentration are presented together with preliminary information on their long-term trends and comparison with model simulation for the different stations. The scope of the present paper is also to provide the necessary suite of information, including data provision procedures, quality control and analysis, data policy, and usage of the ground-based aerosol measurement network. It delivers to users of the World Data Centre on Aerosol, the required confidence in data products in the form of a fully characterized value chain, including uncertainty estimation and requirements for contributing to the global climate monitoring system.
2020
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Open biomass burning has major impacts globally and regionally on atmospheric composition. Fire emissions include particulate matter, tropospheric ozone precursors, and greenhouse gases, as well as persistent organic pollutants, mercury, and other metals. Fire frequency, intensity, duration, and location are changing as the climate warms, and modelling these fires and their impacts is becoming more and more critical to inform climate adaptation and mitigation, as well as land management. Indeed, the air pollution from fires can reverse the progress made by emission controls on industry and transportation. At the same time, nearly all aspects of fire modelling – such as emissions, plume injection height, long-range transport, and plume chemistry – are highly uncertain. This paper outlines a multi-model, multi-pollutant, multi-regional study to improve the understanding of the uncertainties and variability in fire atmospheric science, models, and fires' impacts, in addition to providing quantitative estimates of the air pollution and radiative impacts of biomass burning. Coordinated under the auspices of the Task Force on Hemispheric Transport of Air Pollution, the international atmospheric modelling and fire science communities are working towards the common goal of improving global fire modelling and using this multi-model experiment to provide estimates of fire pollution for impact studies. This paper outlines the research needs, opportunities, and options for the fire-focused multi-model experiments and provides guidance for these modelling experiments, outputs, and analyses that are to be pursued over the next 3 to 5 years. The paper proposes a plan for delivering specific products at key points over this period to meet important milestones relevant to science and policy audiences.
2025
2019
Hulun Lake, the largest inland steppe lake in China, is encountering severe water quality degradation. Estuaries play important roles in material and energetic exchange between rivers and lakes. The water quality at the estuaries of Hulun Lake directly reflects the impact of both human activities and natural factors on the lake’s overall water quality, especially during rainfall events. From July 28, 2021, to August 4, 2021, water samples from 62 sites were collected in the three estuaries of Hulun Lake before and after a moderate rainfall event. 13 water parameters, including dissolved oxygen (DO), Turbidity (Tur), Total Nitrogen (TN), Total Phosphorus (TP), Total Organic Nitrogen (TON), and Total Organic Phosphorus (TOP) were measured. The spatio-temporal distribution of water quality in the estuaries was assessed based on water quality index (WQI). Besides, an improved approach integrating stepwise linear regression (SLR) and principal component analysis (PCA) was utilized to construct a WQImin model for an effective assessment of water quality in these estuaries. Furthermore, the absolute principal component scores-multiple linear regression (APCS-MLR) model was employed to identify and quantify the environmental drivers underlying the water quality in the estuaries. The results of WQI indicated that the water quality of the sites in the estuaries of Hulun Lake was “medium” or “poor”, both before and after the rainfall, with a general deterioration in water quality in response to the rainfall. The simplified WQImin model consisted of 5 crucial parameters (i.e., TN, TP, ammonium (NH4+-N), Tur, and permanganate index (CODMn)), and it performed well without parameter weights. Spatial differences in some water parameters among the estuaries were detected, which were attributed to the natural factors and human activities upstream. The principal environmental factors affecting the water quality in the estuaries consisted of hydrodynamic processes, internal phosphorus release, external phosphorus input, external nitrogen input, nitrification in the estuaries, and external organic input and internal organic release. Therefore, we propose basin management strategies such as limiting grazing pressure, adopting enclosed pasture, wetland restoration, optimizing water renewal cycle in Hulun Lake, and transboundary water quality management to tackle water contamination in Hulun Lake.
2024
2021
We conducted continuous measurements of nanoparticles down to 3 nm size in the Arctic at Mount Zeppelin, Ny Ålesund, Svalbard, from October 2016 to December 2018, providing a size distribution of nanoparticles (3–60 nm). A significant number of nanoparticles as small as 3 nm were often observed during new particle formation (NPF), particularly in summer, suggesting that these were likely produced near the site rather than being transported from other regions after growth. The average NPF frequency per year was 23 %, having the highest percentage in August (63 %). The average formation rate (J) and growth rate (GR) for 3–7 nm particles were 0.04 cm−3 s−1 and 2.07 nm h−1, respectively. Although NPF frequency in the Arctic was comparable to that in continental areas, the J and GR were much lower. The number of nanoparticles increased more frequently when air mass originated over the south and southwest ocean regions; this pattern overlapped with regions having strong chlorophyll a concentration and dimethyl sulfide (DMS) production capacity (southwest ocean) and was also associated with increased NH3 and H2SO4 concentration, suggesting that marine biogenic sources were responsible for gaseous precursors to NPF. Our results show that previously developed NPF occurrence criteria (low loss rate and high cluster growth rate favor NPF) are also applicable to NPF in the Arctic.
2020
Fine-resolution spatio-temporal maps of near-surface urban air temperature (Ta) provide crucial data inputs for sustainable urban decision-making, personal heat exposure, and climate-relevant epidemiological studies. The recent availability of IoT weather station data allows for high-resolution urban Ta mapping using approaches such as interpolation techniques or machine learning (ML). This study is aimed at executing these approaches and traditional numerical modeling within a practical and operational framework and evaluate their practicality and efficiency in cases where data availability, computational constraints, or specialized expertise pose challenges. We employ Netatmo crowd-sourced weather station data and three geospatial mapping approaches: (1) Ordinary Kriging, (2) statistical ML model (using predictors primarily derived from Earth Observation Data), and (3) weather research and forecasting model (WRF) to predict/map daily Ta at nearly 1-km spatial resolution in Warsaw (Poland) for June–September and compare the predictions against observations from 5 meteorological reference stations. The results reveal that ML can serve as a viable alternative approach to traditional kriging and numerical simulation, characterized by reduced complexity and higher computational speeds within the domain of urban meteorological studies (overall RMSE = 1.06 °C and R2 = 0.94, compared to ground-based meteorological stations). The results have implications for identifying the urban regions vulnerable to overheating and evidence-based urban management in response to climate change. Due to the open-sourced nature of the applied predictors and input parsimony, the ML method can be easily replicated for other EU cities.
2023
Negative correlation between soil salinity and soil organic carbon variability
Soil organic carbon (SOC) is vital for terrestrial ecosystems, affecting biogeochemical processes, and soil health. It is known that soil salinity impacts SOC content, yet the specific direction and magnitude of SOC variability in relation to soil salinity remain poorly understood. Analyzing 43,459 mineral soil samples (SOC < 150 g kg−1) collected across different land covers since 1992, we approximate a soil salinity increase from 1 to 5 dS m−1 in croplands would be associated with a decline in mineral soils SOC from 0.14 g kg−1 above the mean predicted SOC (= 18.47 g kg−1) to 0.46 g kg−1 below (~−430%), while for noncroplands, such decline is sharper, from 0.96 above = 35.96 g kg−1 to 4.99 below (~−620%). Although salinity’s significance in explaining SOC variability is minor (<6%), we estimate a one SD increase in salinity of topsoil samples (0 to 7 cm) correlates with respective declines of ~4.4% and ~9.26%, relative to and. The decline in croplands is greatest in vegetation/cropland mosaics while lands covered with evergreen needle-leaved trees are estimated with the highest decline in noncroplands. We identify soil nitrogen, land cover, and precipitation Seasonality Index as the most significant parameters in explaining the SOC’s variability. The findings provide insights into SOC dynamics under increased soil salinity, improving understanding of SOC stock responses to land degradation and climate warming.
2024