Soil erosion is a major degradation process affecting many regions worldwide and impairing the ability of soil to provide ecosystem services. This chapter provides a brief overview of the soil erosion processes and their susceptibility in different land uses, including agricultural, forest, and urban land, as well as different soil management practices that have been implemented to mitigate the problem. Additionally, this chapter synthesizes information regarding the use of remote sensing to support soil erosion assessments, providing examples of remote sensing data used in different studies to support soil erosion modeling.

The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change.

In this case study pharmaceuticals were analysed in the Mondego river (Portugal) and their environmental risk assessed by means of risk quotients based on an extensive retrieval of ecotoxicological data for freshwater and saltwater species. The Mondego river crosses Coimbra, the most populated city in the Portuguese Centro Region hosting a complex of regional hospitals. Environmentally relevant and prioritised pharmaceuticals were investigated in this study and their potential hazards were evaluated by conducting a separate risk assessment for the freshwater and estuary parts of the examined river section. A target analysis approach with method detection limits down to 0.01 ng L−1 was used to determine pharmaceuticals. Twenty-one prioritised target analytes out of seven therapeutical classes (antibiotics, iodinated X-ray contrast media (ICM), analgesics, lipid reducers, antiepileptics, anticonvulsants, beta-blockers) were investigated by applying ultra-high pressure liquid chromatography coupled to a triple quadrupole mass spectrometer equipped with an electrospray ionisation source. The relative pattern of pharmaceuticals along the middle to the lower Mondego showed a quite uniform picture while an approximately 40fold increase of absolute concentrations was observed downstream of the wastewater treatment plant (WWTP) discharge of Coimbra. The most frequently measured substance groups were the ICM, represented by the non-ionic ICM iopromide (βmin: 3.03 ng L−1 - βmax: 2,810 ng L−1). Environmentally more critical substances such as carbamazepine, diclofenac, and bezafibrate, with concentrations up to and 52.6 ng L−1, 59.8 ng L−1, and 10.2 ng L−1 respectively, may potentially affect aquatic wildlife. Carbamazepine revealed elevated risk quotients (RQs >1) along the middle and lower Mondego with a maximum RQ of 53 downstream of Coimbra. Especially for saltwater species, carbamazepine and clarithromycin pose high potential risks. Especially in periods of low water discharge of the Mondego river, other pharmaceuticals as diclofenac and bezafibrate may pose additional risks downstream of the WWTP.

Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large-scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation-wide flood susceptibility mapping, this modeling approach considers ten geo-environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo-environmental input factors. In general, accurate nation-wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well-performing and cost-effective for the case of Sweden, calling for further application and testing in other world regions.

Agricultural land degradation is a global problem affecting food production and other ecosystem services worldwide such as water regulation. It is driven by unsustainable land use and management practices (e.g. intensive tillage, overuse of agrochemicals) and can be aggravated by future climate change. Land degradation is particularly problematic in arid and semi-arid areas of southern Europe, and distinct soil degradation processes impair agricultural areas in Portugal and Greece. This chapter aims to improve understanding of various degradation processes affecting agricultural land, including soil erosion, compaction, contamination, and salinity and sodicity. It summarises the scientific literature on the current status of these degradation processes in agricultural areas of Portugal and Greece and their main causes and consequences. Moreover, it provides examples of best management practices implemented to mitigate agricultural land degradation. Some degradation processes are relatively well documented (e.g. erosion), while knowledge of the spatial extent of others such as soil compaction is still limited. A better understanding of soil degradation processes and of the counter-impacts of improved agricultural management practices is critical to support decision-making and ensure long-term fertility and productivity, thereby maintaining the sustainability of agriculture.

Carbon removals associated with incremental gains in soil organic carbon (SOC) at scale have enormous potential to mitigate global warming, yet confusion over contexts that elicit SOC accrual abound. Here, we examine how bespoke interventions (through irrigation, fertiliser, crop type and rotations), antecedent SOC levels and soil type impact on long-term SOC accrual and greenhouse gas (GHG) emissions. Using a whole farm systems modelling approach informed using participatory research, we discovered an inverse relationship between antecedent SOC stocks and SOC gains realised following intervention, with greater initial SOC levels resulting in lower ex poste change in SOC. We found that SOC accrual was greatest for clays and least for sands, although changes in SOC in sandy loam soils were also low. Diversified whole farm adaptations – implemented through inclusion of grain legumes within wheat/canola crop rotations – were more conducive to improvement in SOC stocks, followed by Intensified systems (implemented through greater rates of irrigation, farm areas under irrigation, nitrogen fertiliser and inclusion of rice and maize in crop rotations). Adaptations that Simplified farm systems by reducing irrigation and fertiliser use resulted in the lowest SOC accrual. In most cases, long-term SOC stocks fell when SOC at the outset was greater than 4–5%, regardless of intervention made, soil or crop type, crop rotation, production system or climate. We contend that (1) management interventions primarily impacted SOC in the soil surface (0–30 cm) and had de minimus impact on deep SOC stocks (30–100 cm), (2) crop rotations including wheat, canola and faba beans were more conducive to improvement in SOC stocks, (3) scenarios with high status quo SOC had little impact on crop productivity, and not necessarily the lowest GHG emissions intensity, (4) productivity and GHG emissions intensity were largely a function of the quantum of nitrogenous fertiliser added, rather than SOC stocks, and (5) aspirations for improving SOC are likely to be futile if antecedent SOC stocks are already high (4–5 %). We conclude that potential for improving SOC stocks exists in contexts where antecedent stocks are low (<1%), which may include regions with land degradation, chronic erosion and/or other constraints to vegetative ground cover that could be sustainably and consistently alleviated.

The quality and vitality of cities largely depend on the design, management, and maintenance of green areas, including urban protected areas (UPAs), since they provide multiple benefits for the city. Due to urbanization and higher anthropogenic pressure, green areas are decreasing which directly affects natural habitats and biodiversity. This study aims to assess soil and vegetation chemical status in UPAs in the city of Belgrade, Serbia, and to understand how their distance from pollution hotspots affects soil and vegetation quality. Additionally, this paper considers the inclusion of soil and vegetation conditions in the urban protected areas management as a basis for introducing a connectivity approach to expand green infrastructure throughout the city. Chemical properties, the content of nutrients (C, N, P, and K), and microelements (Cr, Co, Ni, Cu, Zn, As, Cd, Sn, Pb, Zr, U, and Th) in soil and conifer needles were analyzed. Results showed that the distance of pollution hotspots does not affect nutrient and microelements concentrations in soil, i.e., they do not vary significantly between sites and do not exceed remediation intervention values. However, the microelements status of vegetation is affected since Cr, Cu, Zn, Sn, and Pb are higher in needles from trees from the city center. The state of soil and plant composition supports the establishment of a network of green corridors and should become a part of management strategies, thus helping biodiversity protection, climate change mitigation, and human well-being in the cities. 

Drought is a natural hazard occurring with increasing frequency due to climate change. Drought events reduce soil water content and also soil organic carbon (SOC) content, with negative impacts on crop development and food security. This study investigates the impact of drought on SOC dynamics in agricultural systems and the influence of water availability and farm management practices in these impacts. The manuscript is a systematic review, based on Scopus database for scoping the literature on the topic. A total of 283 records were retrieved, but only 16 papers were relevant for the review. The main findings are: (1) water plays a key role in regulating SOC mineralization due to its impact on dynamics of soil microbial communities, necessitating further research on water management to mitigate carbon losses during drought; (2) different agricultural systems can have differing impacts on SOC under drought conditions depending on crop type (e.g. pastures are more resilient than arable systems) and farm management practices; and (3) SOC loss generally occurs after a drought event, regardless of farm management regime, but the contribution of drought to this loss requires further research. Best management practices, such as cover cropping and soil amendment, can minimize SOC losses, but further research is required to optimize these practices in counteracting the effect of drought. A better understanding of the effects of drought on SOC dynamics, and of short-term and long-term ways to mitigate these effects, is important to ensure soil health and crop productivity.

This study investigates 11 agricultural management practices (AMPs) and their effects on seven visual soil quality indicators and soil aggregate stability. The survey carried out across eight pedoclimatic zones in Europe and China was based on visual soil assessments (New Zealand VSA method) performed on soils subject to different soil management practices and nearby similar soils, under similar farming features, without the distinctive soil management practice (control). Fisher's exact test was used to test if the management treatment was independent of the score of each visual soil quality indicator and to test if the management treatment produced a higher frequency of the score ‘good’. The results showed a statistically significant (α < .05) higher frequency of the score ‘good’ for ‘soil structure and consistency’ and/or ‘soil porosity’ for six AMPs. For no-till AMP, the null hypothesis can also be rejected for ‘susceptibility to erosion’ and ‘soil stability’ and for ‘mulching + permanent soil cover’ AMP, for the ‘presence of tillage pan’ and ‘soil colour’. The hypothesis that the management treatment was independent of the score of each indicator was rejected for ‘soil structure and consistency’ of three AMPs, for ‘soil porosity’ of three AMPs, for ‘soil colour’ of one AMP and for the ‘presence of tillage pan’ of one AMP. This study demonstrates that farming systems sharing a common influential soil management practice at different locations and with different soil types significantly affect the score of some visual soil quality indicators. 

Inappropriate management of municipal solid waste dumpsites is a major cause of groundwater contamination in developing countries, but the extent of the problem is not known. This study investigated groundwater quality in the vicinity of Olusosun dumpsite in Lagos, Nigeria, the most populous city in sub-Saharan Africa. During 2020, monthly groundwater samples were collected in 17 wells and boreholes used as drinking water sources, and analysed for 20 physico-chemical parameters. Differences between sites and seasons were statistically assessed, together with changes in water quality index (WQI). The results indicated that heavy metals (Pb²⁺, Ni⁺, Mn²⁺, Fe²⁺, Cr⁶⁺), cations (Ca²⁺, Mg²⁺, K⁺), total hardness and pH were the main parameters impairing water quality. Drinking water quality standards from both the World Health Organization and Nigeria government were exceeded more often in the wet season than in the dry season. Some groundwater properties were negatively correlated with distance to dumpsite (e.g., Fe²⁺, Pb²⁺, NO₃⁻). Significant differences between sites were identified, but with no clear spatial trend. WQI varied from excellent (6%–24% of the sites over the study period) to unsuitable for drinking water purposes (12%–18%), with good quality prevailing at most sites (35%–47%). Although groundwater quality declined at 24% of the sites over 2020, the results indicated improvements compared with previous decades. Remediation strategies must be implemented to safeguard public health and the sustainability of water resources.