Considering that pesticides have been used in Europe for over 70 years, a system for monitoring pesticide residues in EU soils and their effects on soil health is long overdue. In an attempt to address this problem, we tested 340 EU agricultural topsoil samples for multiple pesticide residues. These samples originated from 4 representative EU case study sites (CSS), which covered 3 countries and four of the main EU crops: vegetable and orange production in Spain (S–V and S–O, respectively), grape production in Portugal (P-G), and potato production in the Netherlands (N–P). Soil samples were collected between 2015 and 2018 after harvest or before the start of the growing season, depending on the CSS. Conventional and organic farming results were compared in S–V, S–O and N–P. Soils from conventional farms presented mostly mixtures of pesticide residues, with a maximum of 16 residues/sample. Soils from organic farms had significantly fewer residues, with a maximum of 5 residues/sample. The residues with the highest frequency of detection and the highest content in soil were herbicides: glyphosate and its main metabolite AMPA (P-G, N–P, S–O), and pendimethalin (S–V). Total residue content in soil reached values of 0.8 mg kg⁻¹ for S–V, 2 mg kg⁻¹ for S–O and N–P, and 12 mg kg⁻¹ for P-G. Organic soils presented 70–90% lower residue concentrations than the corresponding conventional soils. There is a severe knowledge gap concerning the effects of the accumulated and complex mixtures of pesticide residues found in soil on soil biota and soil health. Safety benchmarks should be defined and introduced into (soil) legislation as soon as possible. Furthermore, the process of transitioning to organic farming should take into consideration the residue mixtures at the conversion time and their residence time in soil.

Nitrogen dioxide (NO₂) is one of the most important air pollutants that highly affect the formation of secondary fine particles and tropospheric ozone. In this study based on hourly NO₂ observations from June 2014 to May 2019 and a regional air quality model (WRF−CMAQ), we comprehensively analyzed the spatiotemporal variations of NO₂ concentrations throughout China and in 12 urban agglomerations (UAs) and quantitatively showed the anthropogenic and meteorological factors controlling the interannual variations (IAVs). The ground observations and tropospheric columns show that high NO₂ concentrations are predominantly concentrated in UAs such as Beijing−Tianjin−Hebei (BTH), the Shandong Peninsula (SP), the Central Plain (CP), Central Shaanxi (CS), and the Yangtze River Delta (YRD). For different UAs, the NO₂ IAVs are different. The NO₂ increased first and then decreased in 2016 or 2017 in BTH, YRD, CS, and Cheng−Yu, and decreased from 2014 to 2019 in Harbin−Changchun, CP, SP, Northern Slope of Tianshan Mountain, and Beibu−Gulf, while increased slightly in the Pearl River Delta (PRD) and Hohhot−Baotou−Erdos−Yulin (HBEY). The NO₂ IAVs were primarily dominated by emission changes. The net wintertime decreases of NO₂ in BTH, Yangtze River Middle−Reach, and PRD were mostly contributed by emission reductions from 2014 to 2018, and the significant increase in the wintertime in HBEY was also dominated by emission changes (93%). Weather conditions also have an important effect on the NO₂ IAVS. In BTH and HBEY, the increases of NO₂ in winter of 2016 are mainly attributed to the unfavorable weather conditions and for the significant decreases in the winter of 2017, the favorable weather conditions also play a very important role. This study provides a basic understanding on the current situation of NO₂ pollution and are helpful for policymakers as well as those interested in the study of tropospheric ozone changes in China and downwind areas.

Perennial contaminated groundwater seepage is threatening the downstream ecosystem of the Kazipally Pharmaceutical industrial area located in South India. The sources of seepage are unknown for the last three decades that challenging the regulatory authorities and industries. In general, water quality monitoring and geophysical techniques are applied to identify the sources. However, these techniques may lead to ambiguous results and fail to identify the seepage sources, especially when the area is urbanized/paved, and groundwater is already contaminated with other leakage sources that have similar chemical compounds. In the present study, a novel and multidisciplinary approach were adopted that includes satellite-based Land Surface Temperature (LST) observations, field-based Electrical Resistivity Tomography (ERT), continuous Soil Electrical Conductivity (SEC) and Volumetric Soil Moisture (VSM%) measurements along with groundwater levels monitoring to identify the sources and to control the seepage. The integrated results identified that the locations with the Standard Thermal Anomaly (STA) in the range of −0.5 to -1 °C, VSM% >50%, SEC > 1.5 mS/cm, bulk resistivity < 12 Ω m with shallow groundwater levels < 3 m below ground level (bgl) are potentially contaminated perennial seepage sources. Impermeable sheet piles have been installed across the groundwater flow direction to control the seepage up to 1.5 m bgl, where groundwater frequently intercepts land surface. The quantity of dry season groundwater seepage has been declined by 79.2% after these interventions, which in turn minimized the treatment cost of 1,96,283 USD/year and improved the downstream ecosystem.

Root-triggered microscale variations in O₂ distribution in the rhizosphere of young Phragmites australis are important for nutrient removal in sediments. In this study, the micro-scale O₂ dynamics and the small-scale changes of soluble reactive phosphorus (SRP) and ammonium (NH₄⁺) in the rhizosphere of P. australis were investigated using planar optodes and high-resolution dialysis (HR-Peeper), respectively. Results suggested that root O₂ leakage has a highly variable distribution depending on the stage of root growth, the site of O₂ leakage gradually shift from the entire emerging main roots to the main root tip and subsequently shifted the emerging lateral roots. The O₂ concentration increased in the rhizosphere with increasing light intensity and O₂ levels in the overlying water. Continuous O₂ release from the lateral roots causes the formation of iron plaque on the surface of lateral roots, which reduce the mobility of P by adsorption of iron plaque in the rhizosphere. The oscillation of oxic-anoxic root zones improves nitrogen removal through the processes of anammox, heterotrophic denitrification and nitrification. This work from the micro-scale demonstrates that the O₂ concentration is the spatio-temporal variations in the rhizosphere, and it presents an important role for nutrient removal in sediments.

Petroleum hydrocarbon (PH) contamination of soils remains a major threat to environmental health and food security. A two-years phytoremediation study was conducted on a crude oil polluted soil to assess changes in soil total petroleum hydrocarbon concentration (TPHₛₒᵢₗ) following use of pawpaw seed powder (PSP), moringa seed powder (MSP) and their combination (PSP + MSP) as organic stimulants in cowpea cultivation. The stimulants were tested at different application rates (100, 150, 200 and 250 g m⁻²), with the control (No stimulant) for their effectiveness in reducing TPHₛₒᵢₗ and accelerating the removal rate (R) of PH from soil. The TPHₛₒᵢₗ did not differ significantly (p < 0.05) among the treatments in year 1, but was highest in the control (11,600 mg kg⁻¹) and least in 200 g m⁻² PSP (7400.0 mg kg⁻¹). In year 2, mean TPHₛₒᵢₗ varied significantly (p < 0.05) and remained highest in control (7100 mg kg⁻¹) but lowest in 150 g m⁻² PSP (2700 mg kg⁻¹). Application of 150 g m⁻² PSP gave the highest R (78.2%), followed by 150 g m⁻² PSP+MSP (77.4%), and least by the control (42.7%) over two years of study. The average fresh pod yield of cowpea over two years was highest in 250 g m⁻² PSP (2416.67 kg ha⁻¹), followed by 150 g m⁻² PSP (2173.34 kg ha⁻¹) and least in control (1302.22 kg ha⁻¹). There was significant negative association between TPHₛₒᵢₗ and fresh pod yield (r = −0.403; p < 0.01). However, application of 150 g m⁻² PSP appeared most effective for enhanced phytoremediation of crude oil polluted soil and improvement of cowpea yield.

Pfaffia glomerata is a candidate for the remediation of heavy metal-contaminated soil, but phytoremediation efficiency requires enhancement. In this study, we evaluated how application of DA-6, EDTA, or CA affected the growth and heavy metal accumulation of P. glomerata and soil microorganisms. We found that P. glomerata removed more Cd and Zn than Pb or Cu from contaminated soil. When compared to the control, application of DA-6, CA, or CA + DA-6 increased plant biomass and increased stem Cd concentration by 1.28-, 1.20-, and 1.31-fold respectively; increased leaf Cd concentration by 1.25-, 1.28-, and 1.20-fold, respectively; and increased the total quantity of Cd extracted by 1.37-, 1.37-, and 1.38-fold, respectively. When compared to the control, application EDTA or EDTA + DA-6 significantly increased the soil available metal and Na concentrations, which harmed plant growth. Application of EDTA or EDTA + DA-6 also significantly decreased the Cd concentration in roots and stems. 16S rRNA high-throughput sequencing analysis revealed that application of EDTA or CA alone to soil significantly reduced the richness and diversity of soil bacteria, while foliar spraying of DA-6 combined with EDTA or CA slightly alleviated this reduction. EDTA or CA addition significantly changed the proportion of Actinobacteria and Proteobacteria. In addition, EDTA or CA addition caused changes in soil properties (e.g. heavy metal availability, K concentration, Na concentration, soil pH, soil CEC, and soil DOC concentration) that were associated with changes in the bacterial community. EDTA addition mainly affected the soil bacterial community by changing soil DOC concentration, the soil available Pb and Na concentration, and CA addition mainly affected the soil bacterial community by changing the soil available Ca concentration.

Controlling ammonia (NH₃) emissions has been proposed as a strategy to mitigate haze pollution. To explore the role of NH₃ in haze pollution in Sichuan Basin, where agricultural activities are intense, hourly in situ data of NH₃, as well as nitric acid and secondary inorganic aerosols (SIAs) were gathered in Chengdu from April 2017 to March 2018. We found that NH₃ had an annual mean concentration of 9.7 ± 3.5 (mean ± standard deviation) μg m⁻³, and exhibited seasonal variations (spring > summer > autumn and winter) due to changes in emission sources and meteorological conditions (particularly temperature). Chengdu's atmosphere is generally NH₃-sufficient, especially in the warm seasons, implying that the formation of SIAs is more sensitive to the availability of nitric acid. However, an NH₃ “sufficient-to-deficient” transition was found to occur during winter pollution periods, and the frequency of NH₃ deficiency increased with the aggravation of pollution. Under NH₃-deficient conditions, the nitrogen oxidation ratio increased linearly with the increase in free NH₃, implying that NH₃ contributes appreciably to the formation of nitrate and thus to high PM₂.₅ loadings. No relationships of NH₃ with fossil fuel combustion–related pollutants were found. The NH₃ emissions from farmland and livestock waste in the suburbs of Chengdu and regional transport from west of Chengdu probably contribute to the occurrence of high PM₂.₅ loading in winter and spring, respectively. These results suggest that to achieve effective mitigation of PM₂.₅ in Chengdu, local and regional emission control of NH₃ and NOx synergistically would be effective.

Microplastic pollution is a global concern theme, and there is still the need for less laborious and faster analytical methods aiming at microplastics detection. This article describes a high throughput screening method based on near-infrared hyperspectral imaging (HSI-NIR) to identify microplastics in beach sand automatically with minimum sample preparation. The method operates directly in the entire sample or on its retained fraction (150 μm–5 mm) after sieving. Small colorless microplastics (<600 μm) that would probably be imperceptible as a microplastic by visual inspection, or missed during manual pick up, can be easily detected. No spectroscopic subsampling was performed due to the high-speed analysis of line-scan instrumentation, allowing multiple microplastics to be assessed simultaneously (video available). This characteristic is an advantage over conventional infrared (IR) spectrometers. A 75 cm² scan area was probed in less than 1 min at a pixel size of 156 × 156 μm. An in-house comprehensive spectral dataset, including weathered microplastics, was used to build multivariate supervised soft independent modelling of class analogy (SIMCA) classification models. The chemometric models were validated for hundreds of microplastics (primary and secondary) collected in the environment. The effect of particle size, color and weathering are discussed. Models' sensitivity and specificity for polyethylene (PE), polypropylene (PP), polyamide-6 (PA), polyethylene terephthalate (PET) and polystyrene (PS) were over 99% at the defined statistical threshold. The method was applied to a sand sample, identifying 803 particles without prior visual sorting, showing automatic identification was robust and reliable even for weathered microplastics analyzed together with other matrix constituents. The HSI-NIR-SIMCA described is also applicable for microplastics extracted from other matrices after sample preparation. The HSI-NIR principals were compared to other common techniques used to microplastic chemical characterization. The results show the potential to use HSI-NIR combined with classification models as a comprehensive microplastic-type characterization screening.

Apis mellifera is a pollinator insect model in pesticide risk assessment tests for bees. However, given the economic and ecological importance of stingless bees such as Melipona scutellaris in the Neotropical region, as well as the lack of studies on the effect of insecticides on these bees, toxicity tests for stingless bees should be carried out to understand whether insecticides affect both species of bees in the same manner. Thus, the present study quantified the differential sensitivity of the bees M. scutellaris and A. mellifera to the oral ingestion of the insecticide thiamethoxam by determining the mean lethal concentration (LC₅₀), mean lethal time (LT₅₀), and their effect on the insecticide target organ, the brain. The results showed that the stingless bee is more sensitive to the insecticide than A. mellifera, with a lower LC₅₀ of 0.0543 ng active ingredient (a.i.)/μL for the stingless bee compared to 0.227 ng a.i./μL for A. mellifera. When exposed to a sublethal concentration, morphological and ultrastructural analyses were performed and evidenced a significant increase in spaces between nerve cells of both species. Thus, A. mellifera is not the most appropriate or unique model to determine the toxicity of insecticides to stingless bees.

In this work, a sand and dust storm vulnerability mapping (SDS-VM) approach is developed to model the vulnerability of urban blocks to SDS using GIS spatial analysis and a range of geographical data. The SDS-VM was carried out in Ahvaz, IRAN, representing one of the most dust-polluted cities in West Asia. Here, vulnerability is defined as a function of three components: exposure, sensitivity, and adaptive capacity of the people in the city blocks to sand and dust storms. These components were formulated into measurable indicators (i.e. GIS layers) including: PM₂.₅, wind speed, distance from dust emission sources, demographic statistics (age, gender, family size, education level), number of building floors, building age, land surface temperature (LST), land use, percentage of literate population, distance from health services, distance from city facilities (city center, shopping centers), distance from infrastructure (public transportation, main roads and highways), distance from parks and green spaces, and green area per capita. The components and the indicators were weighted using analytical hierarchy process (AHP). Different levels of risks for the components and the indicators were defined using ordered weighted averaging (OWA). Urban SDS vulnerability maps at different risk levels were generated through spatial multi-criteria data analysis procedure. Vulnerability maps, with different risk levels, were validated against field-collected data of 781 patients hospitalized for dust-related diseases (i.e. respiratory, cardiovascular, and skin). Results showed that (i) SDS vulnerability map, obtained from the developed methodology, gives an overall accuracy of 79%; (ii); regions 1 and 5 of Ahvaz are recognized with the highest and lowest vulnerabilities to SDS, respectively; and (iii) ORness equal to 0 (very low risk) is the optimum SDS-VM risk level for decision-making to mitigate the harmful impacts of SDS in the deposition areas of Ahvaz city.