Environmental Science: Processes & Impacts最新文献

Contamination of heavy metals (HMs) has caused increasing concern due to their ecological toxicities and difficulties in degradation. The transport, retention, and release of HMs in porous media are highly related to their environmental fate and risk to groundwater. Column transport experiments and numerical simulations were conducted to investigate the retention and release behaviors of Cu²⁺, Pb²⁺, Cd²⁺, and Zn²⁺ in the presence and absence of kaolin under varying ionic strengths and cation types. The interaction between HMs and soil colloids is critical to these processes, yet it remains poorly understood. In both single and multi-metal systems, the mobility of HMs ranked as Cd²⁺ > Zn²⁺ > Cu²⁺ > Pb²⁺, is influenced by their hydrolysis ability. Multi-metal systems showed higher mobility due to competition for retention sites, and Ca²⁺ enhanced transport more than Na⁺ due to greater affinity to the sand surface. Kaolin reduced HM transport by adsorption and led to irreversible retention. Cation exchange (Na⁺ replacing Ca²⁺) followed by reduced ionic strength promoted HM release due to the remobilization of kaolin associated with HMs. Uniform, nonmonotonic, and exponential retention profiles indicated variations in the spatial distribution of HMs. The Pb²⁺ and Cu²⁺ were more retained near the column inlet than Cd²⁺ and Zn²⁺, indicating limited mobility in the deep subsurface. Numerical simulations well described HM transport, considering the adsorption and desorption of HMs and the solid–water interface. These results enhance understanding of HM fate in terrestrial environments.

Proton transfer reaction mass spectrometry (PTR-MS) is often employed to characterize gas-phase compounds in both indoor and outdoor environments. PTR-MS measurements are usually made without upstream chromatographic separation, so it can be challenging to differentiate between an ion of interest, its isomers, and fragmentation products from other species all detected at the same mass-to-charge ratio. These isomeric contributions and fragmentation interferences can confound the determination of accurate compound mixing ratios, the assignment of accurate chemical properties, and corresponding analyses of chemical fate. In this study, we deployed a gas chromatograph upstream of a PTR-MS to investigate contributions of isomers and fragmentation products for select indoor air-relevant chemicals. Measurements were made in a test house across a variety of indoor chemical sources, oxidants, and environmental conditions during the Chemical Assessment of Surfaces and Air (CASA) study. Observed confounding signals at each extracted ion chromatogram ranged from 0% (C₂H₆OH⁺, C₈H₂₄O₄Si₄H⁺, and C₁₀H₃₀O₅Si₅H⁺) to 98% (at C₅H₉⁺). For many ions, confounding signals varied between indoor conditions, and there were also differences between confounding signals across indoor vs. outdoor measurements. The relative contribution of sets of key structural isomers (e.g., C₆-C₈ carbonyls, xylenes, trimethylbenzenes, and monoterpenes) remained consistent throughout the measurement period despite changing indoor conditions. These relatively stable isomer distributions yielded stable chemical property assignments for these isomer sets. Taken together, these observations can inform future interpretations of PTR-MS signals measured in different indoor conditions without upstream chromatography.

This study examines the long-term changes in phytoplankton size classes (PSCs) in the Arabian Sea (AS) using the remote sensing reflectance (R_rs) data collected over 12 years (2010–2021) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The R_rs spectra were inverted to chlorophyll-a (Chl-a) concentrations using a non-linear optimisation method, which were then used to estimate the PSC using a region specific three-component model. The analysis is carried out for all four seasons, i.e., winter (December–February), pre-monsoon (March–May), monsoon (June–September) and post-monsoon (October–November). A machine learning random forest (RF) model is employed to predict the seasonal and long-term variability in PSCs and to quantify the influence of environmental drivers. The seasonal climatology of three size classes – micro (larger), nano (medium-sized), and pico (smaller) – reveals that micro-phytoplankton predominantly occupy the northern AS during winter and pre-monsoon seasons, contributing over 50% to the total Chl-a. During the monsoon season, a significant rise in micro-phytoplankton contribution (60–80%) is noted off the coasts of Somalia, Oman and Kerala due to strong upwelling. In contrast, nano-phytoplankton contributions are minimal during the pre-monsoon season but remain fairly consistent in other seasons, and pico-phytoplankton dominates the oligotrophic waters of the central and southern AS during pre- and post-monsoon. The analysis of PSCs from 2010 to 2021 shows a strong decreasing trend in micro-phytoplankton concentration (−0.13 ± 0.19 mg m⁻³ year⁻¹), accompanied by a steady increase in pico-phytoplankton (0.0009 ± 0.0005 mg m⁻³ year⁻¹) and nano-phytoplankton (0.001 ± 0.0009 mg m⁻³ year⁻¹). To elucidate these long-term trends, RF model was instrumental in identifying key environmental drivers, with sea surface temperature (SST) emerging as the most influential factor affecting pico- and micro-phytoplankton. The feature importance scores for SST are highest during winter and pre-monsoon for both pico-phytoplankton and micro-phytoplankton, underscoring the sensitivity of these classes to temperature changes. RF model also highlights the role of mixed layer depth (MLD) and wind speed (WS) in driving the seasonal shifts in PSCs, particularly during the monsoon and post-monsoon periods. These findings suggest that the rise in SST, coupled with changes in vertical mixing and stratification, drives the shift towards smaller cells, mainly pico-phytoplankton in the AS. This shift towards smaller cells indicates a possible decline in marine food chain efficiency, reduced carbon export rates and declining primary productivity—a real concern for food security in the region.

Emission rates for volatile organic compounds (VOCs) have been quantified from frying, spice and herb cooking, and cooking a chicken curry, using real-time selected-ion flow-tube mass spectrometry (SIFT-MS) for controlled, laboratory-based experiments in a semi-realistic kitchen. Emissions from 7 different cooking oils were investigated during the frying of wheat flatbread (puri). These emissions were dominated by ethanol, octane, nonane and a variety of aldehydes, including acetaldehyde, heptenal and hexanal, and the average concentration of acetaldehyde (0.059–0.296 mg m⁻³) and hexanal (0.059–0.307 mg m⁻³) measured during the frying was 2–10 times higher than the recommended limits for indoor environments. Total VOC emission rates were greatest for ghee (14 mg min⁻¹), and lowest for groundnut oil (8 mg min⁻¹). In a second series of experiments, 16 herbs and spices were individually shallow-fried in rapeseed oil. Over 100 VOCs were identified by offline gas chromatography-mass spectrometry (GC-MS), and absolute emission rates as well as oxidant reactivity for a subset of four spices were determined. These experiments allowed distinct indoor air quality profiles to be calculated for individual oils, herbs and spices, which were used to inform and interpret more realistic cooking experiments where a full recipe of chicken curry was prepared. Total-mass VOC emissions from chicken curry were dominated by methanol (62%), monoterpenes (13%) and ethanol (10%). Additionally, a clear relationship between the cooking events and the chemical classes of VOC was observed, e.g. heating the oil (aldehydes), frying spices (monoterpenes) and adding vegetables (alcohols).

The nano-self-assembly of natural organic matter (NOM) profoundly influences the occurrence and fate of NOM and pollutants in large-scale complex environments. Machine learning (ML) offers a promising and robust tool for interpreting and predicting the processes, structures and environmental effects of NOM self-assembly. This review seeks to provide a tutorial-like compilation of data source determination, algorithm selection, model construction, interpretability analyses, applications and challenges for big-data-based ML aiming at elucidating NOM self-assembly mechanisms in environments. The results from advanced nano-submicron-scale spatial chemical analytical technologies are suggested as input data which provide the combined information of molecular interactions and structural visualization. The existing ML algorithms need to handle multi-scale and multi-modal data, necessitating the development of new algorithmic frameworks. Interpretable supervised models are crucial owing to their strong capacity of quantifying the structure–property–effect relationships and bridging the gap between simply data-driven ML and complicated NOM assembly practice. Then, the necessity and challenges are discussed and emphasized on adopting ML to understand the geochemical behaviors and bioavailability of pollutants as well as the elemental cycling processes in environments resulting from the NOM self-assembly patterns. Finally, a research framework integrating ML, experiments and theoretical simulation is proposed for comprehensively and efficiently understanding the NOM self-assembly-involved environmental issues.

Fenpyrazamine (FPA) is a widely used fungicide in agriculture to control fungal diseases, but its environmental degradation by oxidants and the formation of potential degradation products remain unexplored. This study investigates the oxidation of FPA by hydroxyl radicals (HO˙) using density functional theory (DFT) calculations at the M06-2X/6-311++G(3df,3pd)//M06-2X/6-31+G(d,p) level of theory. Three standard oxidation mechanisms, including formal hydrogen transfer (FHT), radical adduct formation (RAF), and single electron transfer (SET), were evaluated in the aqueous phase, with reaction kinetics analyzed over a temperature range of 283–333 K. As a result, the reactivity order of the mechanisms was determined to be RAF > FHT > SET. At 298 K, the calculated total rate constants for FHT and RAF reactions were competitive, being 6.09 × 10⁹ and 8.21 × 10⁹ M⁻¹ s⁻¹, respectively, while that for SET was slightly lower at 2.35 × 10⁹ M⁻¹ s⁻¹. The overall rate constant was estimated to be 1.67 × 10¹⁰ M⁻¹ s⁻¹. The most favourable RAF reaction occurred at the C38C39 double bond, while the predominant FHT reactions involved the H15 and H13 hydrogen atoms of the methyl C8 group. The lifetime of FPA in natural water with respect to HO˙ oxidation was predicted to range from 10.84 hours to 2.62 years, depending on environmental conditions. Furthermore, the toxicity assessments revealed that while FPA is neither bioaccumulative nor mutagenic, it poses developmental toxicity and is harmful to aquatic organisms, including fish, daphnia, and green algae.

Gas/particle (G/P) partitioning is a core process governing the atmospheric transport of organophosphate flame retardants (OPFRs). However, accurately predicting the G/P partition performance of OPFRs remains a challenge. In this study, four independent models were employed to estimate the characteristics of OPFR G/P partitioning within the octanol–air partition coefficient range of 4.7 (TMP) to 14.2 (TMPP). The results showed that in the maximum partition domain, the Li–Ma–Yang steady-state model fitted the best, with 85.2% of the predicted G/P partition quotient (log K_P) values within an acceptable deviation range of ±1 log units for OPFRs. Accordingly, no significant deviations were observed between the predicted (0.56 ± 0.32) and monitored (0.52 ± 0.11) values of the average particle-bound fraction (φ_P) for the Li–Ma–Yang model in the maximum partition domain. Large deviations were observed between the monitored values and predicted log K_P values by these four models in the equilibrium domain. Several factors responsible for the significant deviations observed in G/P partitioning values of OPFRs were discussed. These identified factors were used to develop a new empirical equation, which substantially improved log K_P predictions for OPFRs to 75.8% in the equilibrium domain.

This article emphasizes the crucial role of metrology, the science of measurement, in modern life. It explores the history and importance of the global measurement system in ensuring reliable and comparable data; how this system has evolved over the years into what we now recognise as the International System of Units; and the recent changes that have future proofed our system of measurement against the challenges of technological developments yet to come. The text highlights the particular significance of accurate measurements for air quality studies as having direct impact on policy decisions and assessment of the health effects of air pollution. To enhance the credibility and efficiency of air quality research, the article advocates for widespread adoption the principles of accreditation – the independent assessment and recognition of one's measurement capabilities – to strengthen the confidence in the conclusions made by air quality studies and thereby improve the discipline's effectiveness in supporting and assessing evidence-based policies to reduce air pollution.

The migration behavior of microplastics in water is affected by many factors; in particular, the migration mechanism of microplastics in the terrestrial freshwater environment is more complicated than that in the marine environment. In order to understand the migration behavior of microplastics in the freshwater environment, the hydraulic parameter thresholds of different types of microplastics in water were identified based on hydraulic experiments and force analysis methods. The results show that the motion state of microplastics is affected by their own internal factors and external environmental factors, and the flow rate is the key external factor affecting the change of their motion state. In the vertical direction, the higher the density, the rougher the environment, and the closer the shape to the flake, the greater the critical starting flow velocity and the critical resuspension flow velocity. The settling velocities, critical initiation velocities, and critical resuspension velocities of microplastics range from 0.05 to 0.17 m s⁻¹, 0.03 to 0.44 m s⁻¹, and 0.251 to 0.83 m s⁻¹, respectively. Horizontally, the bottom rolling velocities of microplastics vary significantly. These velocities are positively correlated with water flow velocity but are inversely proportional to the density of the microplastics and the roughness of the substrate. By combining experimental data, mathematical expressions for the critical hydraulic parameters of microplastics were derived, showing improved accuracy compared to traditional methods. This paper explores the trajectory of different types of microplastics after entering the water body and analyzes their migration mechanism in the river. The research results have certain theoretical guiding significance for revealing the migration law of microplastics in the freshwater environment.

Nitrate ions (NO₃⁻) are one of the most common contaminants in the groundwater of the Zagreb alluvial aquifer, which hosts strategic groundwater reserves of the Republic of Croatia and supplies drinking water to one million inhabitants of the capital city. To better understand the origin and the dynamics of NO₃⁻ in the unsaturated and saturated zones, the stable isotopes of nitrogen (δ¹⁵N) and oxygen (δ¹⁸O) in dissolved nitrate, combined with physico-chemical, hydrogeochemical and water stable isotope data, were used in the current work, together with statistical tools and mixing models. The study involved monthly sampling of groundwater, surface water, precipitation and soil water samples. Additionally, the isotopic composition of total nitrogen (δ¹⁵N_bulk) was determined in solid samples representing the local nitrate sources. The combination of a nitrous oxide isotopic analyzer and the titanium(III) reduction method provides reliable measurements of δ¹⁵N_NO3 and δ¹⁸O_NO3, with optimal stability achieved under specific conditions. Nitrate in the study area predominantly originates from organic sources, with nitrification as the main biogeochemical process, while denitrification was identified at sampling sites under specific anaerobic conditions. Although statistical analysis can be a valuable tool, it should be applied with caution if NO₃⁻ originates from multiple sources. The isotopic composition of water showed that groundwater is predominantly recharged by the Sava River but its contribution varied spatially. The results also show the existence of a different recharge source in the southern part of the aquifer. Our findings highlighted the importance of employing a diverse range of analytical methods to obtain reliable and comprehensive understanding of nitrate contamination. By integrating multi-method approaches, stakeholders can better understand the complexities of groundwater contamination and implement more targeted measures to safeguard the water supplies for future generations.