Environmental Monitoring and Assessment最新文献

{"title":"Plant family identity drives arbuscular mycorrhizal fungal spore abundance and community structure in natural rangelands","authors":"Şahin Palta,&nbsp;Eren Baş,&nbsp;Zekeriya Yavaşcı","doi":"10.1007/s10661-026-16087-2","DOIUrl":"10.1007/s10661-026-16087-2","url":null,"abstract":"<div><p>Arbuscular mycorrhizal fungi (AMF) are important components of belowground biotic networks, yet variation in AMF spore abundance and community structure among plant families in natural rangelands remains insufficiently understood. This study investigated AMF spore abundance, community composition, and rhizosphere soil properties across five plant families in a natural rangeland in northwestern Türkiye. Variation in soil organic carbon, total nitrogen, available potassium, CaCO₃ content, and AMF spore density was observed among the sampled plant groups. AMF spore density was highest in Asteraceae (609.8 spores 50 g⁻¹ soil) and lowest in Urticaceae (77.6 spores 50 g⁻¹ soil). Pearson correlation analysis showed a positive association between AMF spore density and soil pH (<i>r </i>= 0.47) and negative associations with electrical conductivity (EC; <i>r</i> =  − 0.49), available potassium (K; <i>r</i> =  − 0.44), and total nitrogen (N; <i>r</i> =  − 0.40). <i>Acaulospora tuberculata</i>, <i>Claroideoglomus etunicatum</i>, <i>Funneliformis geosporum</i>, <i>Glomus</i> sp., and <i>Rhizophagus clarus</i> occurred in the rhizosphere soils of all five plant families, indicating broad distribution across the investigated host groups. Species richness was highest in Urticaceae, whereas Shannon diversity and evenness were highest in Lamiaceae; Fabaceae and Asteraceae showed lower diversity values. Jaccard similarity was highest between Fabaceae and Asteraceae. Overall, AMF spore abundance and community patterns varied among the sampled plant groups and were associated with rhizosphere soil conditions in the investigated rangeland.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 11","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial distribution characteristics and source analysis of heavy metals in river sediments in Western Hainan Island, China","authors":"Jiaqing Li,&nbsp;Shuwen Sun,&nbsp;Yanqing Wang,&nbsp;Fuqiang Wang,&nbsp;Jinli Wang,&nbsp;Xiaoqi Chen","doi":"10.1007/s10661-026-16058-7","DOIUrl":"10.1007/s10661-026-16058-7","url":null,"abstract":"<div><p>The spatial distribution and sources of heavy metals in river sediments can effectively reflect watershed geochemical processes. Hainan Island, the only tropical island province in China, is located in the country’s southern region, with eight major rivers in its western region chosen as the study area. A total of 67 surface sediment samples were collected to analyze six heavy metals (Cr, Cd, Pb, Zn, Cu, Ni). Contamination levels, ecological risks, and source contributions were assessed using the contamination factor (CF), geo-accumulation index (<i>I</i>_geo), potential ecological risk index (RI), and APCS-MLR model. The average metal concentrations followed the order: Zn (41.05 mg/kg) &gt; Pb (30.93 mg/kg) &gt; Cr (17.89 mg/kg) &gt; Cu (8.23 mg/kg) &gt; Ni (5.45 mg/kg) &gt; Cd (0.08 mg/kg), with CV values ranging from 41.3% to 181.5%. The study area presented an overall low ecological risk. Pb (mean CF = 1.21) and Cd (mean CF = 1.08) exhibited moderate contamination. Cr, Zn, Cu, and Ni showed no contamination (CF &lt; 1). The mean RI value was 39.70, indicating low overall ecological risk. APCS-MLR results identified three pollution sources: the dominant natural-agricultural-traffic composite source (64.0%), mainly controlling Cd, Pb, and Zn enrichment; the industrial source (13.3%), primarily affecting Cr, Ni, and Cu; and the mixed source (22.7%), representing multi-source superposition. This study clarifies the pollution characteristics and key sources of heavy metals in western Hainan Island river sediments, providing theoretical support for regional pollution management and watershed ecological restoration.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 11","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From reservoir to plate: monitoring irrigation water source as the dominant driver of vegetable microbial contamination risk in Sahelian smallholder systems","authors":"Adama Pamba Séré,&nbsp;Abdoudramane Sanou,&nbsp;Edwige Noëlle Roamba,&nbsp;Nabèrè Ouattara,&nbsp;Roger Dakuyo,&nbsp;Kabakdé Kaboré,&nbsp;Kiessoun Konaté,&nbsp;Donatien Kaboré,&nbsp;Mamoudou Hama Dicko","doi":"10.1007/s10661-026-16056-9","DOIUrl":"10.1007/s10661-026-16056-9","url":null,"abstract":"<div><p>Consumption of raw vegetables is a major foodborne exposure pathway in sub-Saharan Africa, yet the relative contribution of irrigation water quality versus on-farm hygiene practices to human contamination risk has rarely been quantified using a convergent, multi-method statistical framework. This study determined the relative weight of irrigation water source and Good Agricultural/Hygiene Practices (GAP/GHP) compliance as drivers of microbiological contamination, and thus human exposure risk, for raw vegetables in Northern Burkina Faso. Lettuce (<i>n</i> = 66) and tomato (<i>n</i> = 54) were sampled from 120 farms across three sites irrigated with reservoir, well, or borehole water. GAP/GHP compliance was scored by field audit; <i>Escherichia coli</i> and <i>Salmonella</i> spp. were quantified by ISO reference methods and analysed using ANOVA, logistic regression, random forest modelling, principal component analysis and k-means clustering. Irrigation water source was the dominant predictor of <i>E. coli</i> load (F = 61.6, <i>p</i> &lt; 0.001, eta-squared = 0.51), with mean loads of 3.00, 2.25 and 1.19 log CFU/g for reservoir, well and borehole water respectively; no <i>Salmonella</i> was detected in borehole-irrigated samples. Reservoir water was the primary <i>Salmonella</i> risk factor (OR = 3.82, 95% CI 1.29–11.3). Random forest modelling confirmed water source as the leading predictor (41.1% importance) ahead of hygiene practices (9.8%), and this ranking was stable across five independent analytical methods. We translate these findings into a field-deployable Water-Security Risk Score for extension services, and situate irrigation water source as a broadly relevant human exposure pathway across smallholder vegetable systems in the wider Sahel.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 11","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical properties of colored dissolved organic matter and their importance in the hypersaline Arabian Gulf to assess the status of the ecosystem and its influence on the northwestern Indian Ocean biogeochemistry: a review","authors":"Turki Al-Said,&nbsp;N. V. H. K. Chari,&nbsp;Rakhesh Madhusoodhanan","doi":"10.1007/s10661-026-16024-3","DOIUrl":"10.1007/s10661-026-16024-3","url":null,"abstract":"<div><p>This review summarizes the existing knowledge on the sources, composition, transformation, and biogeochemical importance of dissolved organic matter (DOM) in aquatic environments, based on observations of its optically measurable fraction, known as chromophoric dissolved organic matter (CDOM). Optical methods, including UV–visible absorption and fluorescence spectroscopy, spectral radiometry, and hyperspectral satellite remote sensing, provide valuable information on the distribution and dynamics of DOM, which are closely linked to physical transport, microbial transformations, and photochemical degradation in coastal and marine environments. The Arabian Gulf (AG) represents a distinct marine environment characterized by shallow bathymetry, hypersalinity, large seasonal temperature variability, semienclosed circulation, restricted water exchange, and relatively long residence times. These conditions, together with intense solar radiation, osmotic stress, limited terrestrial freshwater inputs, and increasing anthropogenic pressures, can strongly influence the sources, composition, and transformation of DOM. Within this dynamic environment, DOM may undergo transformations driven by seasonal biological production, benthic–pelagic interactions, sedimentary processes, and anthropogenic inputs, followed by further biological and photochemical processing. The formation of Persian Gulf water (PGW) mass in the AG and its export through the Strait of Hormuz into the subsurface waters of the northern Arabian Sea as a density-driven outflow may influence oxygen minimum zone (OMZ) dynamics and contribute to carbon biogeochemical cycling in the northern Indian Ocean. Despite the importance of these processes, significant knowledge gaps remain regarding the sources, composition, transformation, and fate of CDOM and its role in carbon cycling in the AG. Consequently, integrated in situ and remote sensing approaches for characterizing CDOM are essential to improve our understanding of DOM dynamics and carbon biogeochemical processes in the AG and in the northern Indian Ocean, particularly in the context of ongoing environmental and global climate change.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 11","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling freshwater inflow to a tropical estuary using stochastic and neural-network approaches under contrasting hydroclimatic conditions in Brazil","authors":"Luísa Maria de Souza Viana,&nbsp;Eloá Corrêa Lessa Tostes,&nbsp;Wendel Dias Constantino,&nbsp;Felipe Henrique Rossi Luze,&nbsp;Marcos Sarmet Moreira de Barros Salomão,&nbsp;Taíse Bonfim de Jesus,&nbsp;Carlos Eduardo Veiga de Carvalho","doi":"10.1007/s10661-026-16034-1","DOIUrl":"10.1007/s10661-026-16034-1","url":null,"abstract":"<div><p>Freshwater inflow regulates estuarine circulation, salinity gradients, sediment transport, and biogeochemical fluxes, yet long-term discharge records remain scarce in many tropical coastal basins. We compared linear regression, stochastic time-series models (ARMA, ARIMA, and SARIMA), and multilayer perceptron (MLP) neural networks for reconstructing monthly discharge at the Ituberá gauging station, upstream of the Serinhaém estuary (southern Bahia, Brazil), using records from 1966 to 2019. No model family was uniformly superior across rolling-origin selection and independent validation. Predictor-matched comparisons showed no general advantage of MLPs over linear formulations, and extending calibration through 2011 did not consistently improve post-2012 performance. Mean monthly discharge was approximately 15% lower after 2012, while changes in variance and the general seasonal pattern were not detected and evidence for altered temporal dependence remained inconclusive. One-step reconstructions transferred more consistently to held-out data than recursive and longer-horizon predictions, which showed error accumulation. The reconstructed series provides an upstream freshwater boundary indicator for future estuarine modeling, while ecological consequences require explicit hydrodynamic and biogeochemical simulation.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 11","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10661-026-16034-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature-constrained conditional generative data augmentation for hyperspectral retrieval of nitrogen and phosphorus in inland waters under small-sample conditions","authors":"Ruichun Chang,&nbsp;Feilong Wang,&nbsp;Wanquan Tuo","doi":"10.1007/s10661-026-16039-w","DOIUrl":"10.1007/s10661-026-16039-w","url":null,"abstract":"<div><p>Hyperspectral retrieval of total nitrogen (TN) and total phosphorus (TP) is constrained by the scarcity of field spectra paired with laboratory measurements. We developed a feature-constrained conditional generative adversarial network (F-CGAN) for a 46-site inland-river dataset. Its generator maps noise and target concentration to a 651-band spectrum, while a frozen partial least-squares regression (PLSR) supervisor applies a feature-consistency penalty within target-specific windows identified from Pearson and Spearman correlations. A 36-site development set was used for feature selection, model fitting, and tuning, and 10 sites were held out for final evaluation. Synthetic-spectral fidelity was assessed using t-distributed stochastic neighbor embedding (t-SNE), maximum mean discrepancy (MMD), and a principal-component-based Fréchet distance (PCA-FD); predictive utility was compared across five regression models. The hybrid one-dimensional convolutional neural network–Transformer (Hybrid) increased <span>(R^2)</span> and reduced root mean square error (RMSE) at every augmentation ratio relative to the unaugmented baseline. Across 100–400%, RMSE reductions ranged from 6.19 to 13.68% for TN and from 2.80 to 16.94% for TP. At 400%, Hybrid achieved TN/TP <span>(R^2)</span> values of 0.8726/0.8800 and RMSEs of 2.6820/0.0600 mg L<span>(^{-1})</span>. In 10,000 paired Bootstrap resamples, the proportions favoring lower RMSE ranged from 83.47 to 98.83% for TN and from 92.79 to 99.61% for TP. After Holm correction across the eight target–ratio comparisons, only TP at 400% remained below 0.05; the other comparisons, including TN at 400%, were not statistically significant. The results indicate a broadly favorable but non-monotonic and architecture-dependent augmentation response, while uncertainty remains because of the small independent test set.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 11","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Iran’s methane super-emitters: assessment of discrepancies between satellite observations and inventories","authors":"Hossein Maazallahi,&nbsp;Fathollah Pourfayaz,&nbsp;Maryam Avishan","doi":"10.1007/s10661-026-16045-y","DOIUrl":"10.1007/s10661-026-16045-y","url":null,"abstract":"<div><p>This study presents a satellite-based assessment of methane (CH₄) super-emitters (&gt; 100 kg h⁻¹) across Iran’s oil and gas (O&amp;G) and waste sectors from 2022 to 2025. Annualized top-down emissions from O&amp;G super-emitters ranged from 1390 ± 70 kton year⁻¹ to 6500 ± 200 kton year⁻¹, and waste-sector emissions from 110 ± 20 kton year⁻¹ to 630 ± 70 kton year⁻¹, with inter-annual differences largely reflecting source variability and the study’s focus on super-emitters. An independent bottom-up IPCC Tier-1 recalculation for Iran's solid waste disposal sites yielded 700 [160–1900] kton year⁻¹ of CH₄ emissions, substantially exceeding both Iran’s 3^rd National Inventory Report (23 kton year⁻¹) and EDGAR v8.0 (400–500 kton year⁻¹). Seasonal CH₄ emission patterns were identified, with O&amp;G emissions peaking in warmer months and waste emissions in cooler months. While satellite-based observations are useful, following our communications with the National Iranian Petrochemical Company (NIPC) we concluded that some of satellite-based reported CH₄ emission points were questionable, possibly due to spectral overlap of CH₄ with other hydrocarbons and/or spatial misattribution. Satellite observations require complementary ground-based and airborne measurements at source- and site-levels for robust quantification, attribution, and verification. Overall, the results reveal that Iran requires a comprehensive focus on advancing emission monitoring, reporting and verification for emission inventories, which have not been updated since 2017, and highlight considerable mitigation potential through flare-gas utilization for domestic or industrial uses, landfill gas recovery, and engagement with international initiatives such as Oil and Gas Methane Partnership 2.0 (OGMP 2.0) and International Methane Emission Observatory (IMEO).</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 11","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing divergent ecological responses to forest and grassland dynamics using RSEI and interpretable machine learning in Northern Hebei, China","authors":"Baoni Xie,&nbsp;Huiyan Li,&nbsp;Wei Li,&nbsp;Zhanfei Qin,&nbsp;Yanfang Wang,&nbsp;Hengtao Zuo,&nbsp;Ji Zhu,&nbsp;Shuhui Yu","doi":"10.1007/s10661-026-16061-y","DOIUrl":"10.1007/s10661-026-16061-y","url":null,"abstract":"<div><p>Vegetation gains and losses may exhibit unequal or counterintuitive ecological responses because of differences in their initial conditions and land use transition pathways. In this study, we quantified changes in the remote sensing ecological index (RSEI) associated with forest and grassland dynamics in northern Hebei, China during from 2000 to 2022. We assessed asymmetry in the RSEI responses associated with forest and grassland gain and loss and used eXtreme Gradient Boosting with Shapley Additive Explanations (XGBoost-SHAP) model to identify the principal environmental driving factors. The results indicated that mean RSEI increased by 29.79%, and 52.38% of the study area showed a substantial increase (0.005 a⁻¹, <i>P</i> &lt; 0.05). Forest gain was consistently associated with positive adjusted RSEI responses, whereas forest loss was associated with negative responses. Significant mirror asymmetry occurred only during from 2004 to 2012 (<i>A</i>_mirror = − 0.049), when the negative response associated with forest loss exceeded the positive response associated with forest gain. Grassland gain was associated with lower RSEI and grassland loss with higher RSEI, whereas their aggregate asymmetry was not statistically supported. The reversed grassland responses were closely related to their transition pathways because grassland gain originated mainly from cropland and was associated with lower RSEI, whereas grassland loss areas transitioned mainly to forest or cropland and were associated with higher RSEI. Across from 2000 to 2022, DEM (28.04%) and evapotranspiration (ET, 20.59%) were the leading predictors of forest gain, whereas nighttime light (NTL, 22.66%) and precipitation (20.20%) ranked highest for forest loss. Grassland gain was primarily associated with ET (34.50%) and precipitation (15.88%), while ET (20.65%) and DEM (20.48%) ranked highest for grassland loss. SHAP dependence relationships were non-linear and varied among vegetation types, change directions and study periods. These findings show that vegetation area change alone cannot adequately indicate ecological improvement and that gain and loss should be interpreted relative to stable conditions and specific land use pathways.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 11","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of ecological environment quality and analysis of driving forces in the Taklamakan Desert based on arid remote sensing ecological index","authors":"Na Jiang,&nbsp;Jianmin Liu,&nbsp;Yihua Chen,&nbsp;Yaoqing Xu,&nbsp;Defeng Shang,&nbsp;Hui Zhao","doi":"10.1007/s10661-026-16065-8","DOIUrl":"10.1007/s10661-026-16065-8","url":null,"abstract":"<div><p>To quantitatively assess ecological environment quality in the Taklamakan Desert, Landsat imagery for 2010, 2014, 2018 and 2022 was obtained through GEE. The traditional remote sensing ecological index (RSEI) was modified to construct the arid remote sensing ecological index (ARSEI). ARSEI applicability was evaluated against RSEI. Difference analysis characterized ecological changes from 2010 to 2022. Geodetector was used to identify factors affecting the 2022 ARSEI spatial pattern and their interactions. The results showed that ARSEI had good applicability. Its mean PC1 variance contribution was 85.58%, approximately 4.0 percentage points higher than RSEI, and its mean absolute correlation with the five ecological indicators was 0.79. Mean ARSEI values were 0.1626, 0.2007, 0.1390, and 0.2030. Ecological quality remained relatively low, but improved in 2022 relative to 2010. Ecological improvement was concentrated in oases and along the Tarim, Hotan, and Yarkand River corridors, while degradation mainly occurred in oasis desert transition zones and areas with frequent wind erosion. Improvement, degradation, and no change accounted for 10.00%, 5.62%, and 84.39%, respectively, with improvement exceeding degradation. Geodetector results showed that explanatory power was ranked as vegetation coverage, precipitation, land use, mean annual wind speed, elevation, and slope. Vegetation coverage had the highest explanatory power (<i>q </i>= 0.3102), followed by precipitation (<i>q </i>= 0.2359). Most factor combinations presented enhanced effects. The interaction between vegetation coverage and precipitation showed the highest explanatory power (<i>q</i> = 0.7104) with nonlinear enhancement, which was stronger than the explanatory power of individual factors. This study provides a reference for remote sensing assessment, dynamic monitoring, and ecological management in extremely arid desert regions.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 11","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-analytical screening of pesticides, phenols, heavy metals, and hydrocarbons in sediments from industrialized sites: a preliminary forensic interpretation","authors":"Mohamed Loutis,&nbsp;Djillali Meftali,&nbsp;Tahar Benayad,&nbsp;Fayrouz Djellouli,&nbsp;Abdallah Dahmani","doi":"10.1007/s10661-026-16055-w","DOIUrl":"10.1007/s10661-026-16055-w","url":null,"abstract":"<div><p>A multi-analytical environmental forensic screening of pesticides, phenols, heavy metals, and hydrocarbons is performed on four representative sediment samples (S1–S4), selected from an initial set of 18 samples and collected at El Harrach, Mohammadia, Bordj El Kiffan, and Oued Smar in the Algiers area between February and July 2025, to establish a first-level contamination fingerprint supporting future source-discrimination studies. Thin-layer chromatography (TLC) and gas chromatography–mass spectrometry (GC–MS) are used for pesticide screening, Fourier-transform infrared spectroscopy (FTIR) is used for phenol screening, atomic absorption spectroscopy (AAS) is used for heavy metal determination, and automatic thermal desorption gas chromatography–mass spectrometry (ATD–GC–MS) is used for hydrocarbon analysis. The targeted organochlorine pesticides (OCPs) and organophosphorus pesticides (OPPs) are not detected under the applied screening conditions, and no FTIR-identifiable phenolic signatures are observed. Compound-specific GC–MS limit of detection (LOD) values range from 0.012 to 0.029 mg kg⁻¹ and limit of quantification (LOQ) values range from 0.036 to 0.088 mg kg⁻¹. Trace-level phenolic occurrence cannot be excluded by the FTIR screening approach. In contrast, all samples contain measurable concentrations of cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), lead (Pb), nickel (Ni), and zinc (Zn), with spatial variability. geoaccumulation index (<i>I</i>_geo) values indicate moderate to heavy contamination for selected metals, while sediment quality guideline (SQG) comparison reveals exceedances of the probable effect level (PEL) for Pb, Ni, and Zn. The pollution load index (PLI) exceeds unity at all sites. Hydrocarbon screening reveals recurrent aliphatic compounds in all samples, consistent with petroleum-derived or urban-industrial inputs. The combined metal–hydrocarbon profiles provide site-specific contamination fingerprints that differentiate between sampling locations and constitute a baseline dataset for future targeted source apportionment, ecological risk assessment, and sediment quality monitoring at the investigated industrialized sites in the Algiers area.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"198 11","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149189705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}