Suspended Matter Concentration Research Articles

Gaussian process regression as a powerful tool for analysing time series in environmental geochemistry

Monitoring programs require more advanced data management for the registered time series. Classical temporal series decomposition cannot fulfil current needs regarding adequate data representation, optimization of the spatial-temporal sampling resolution and predictive power. In the manuscript at hand, we will demonstrate that Gaussian process regression (GPR) models are a vital machine-learning tool to interpret temporal series, improving understanding of geochemical cycles, providing input data for geochemical models and acting as a guide for future decisions in environmental monitoring. Firstly, we explore the impacts of sampling frequency in the GPR performance for temporal series with variable lengths and sampled frequencies of water discharges. On a second approach, we present the strengths and weaknesses between classical decomposition of temporal series and GPR results for a case study: a 14-year record of water discharge, suspended particulate matter and antimony concentrations in the Garonne River. Our results suggest that (i) even short temporal series with low sampling resolution can be accurately characterized by GPR when presenting well defined seasonal patterns, and (ii) GPR provides more detailed and robust support than classical statistics to identify processes responsible for multi-scale geochemical signals. This work provides a reference for researchers, engineers, and stakeholders for more reliable monitoring, understanding, and managing aquatic ecosystems.

Assessment of GOCI-II satellite remote sensing products in Lake Taihu

IntroductionThe Geostationary Ocean Color Imager-II (GOCI-II), launched on February 19, 2020, offers increased observation times throughout the day and higher spatial resolution compared to its predecessor, the Geostationary Ocean Color Imager (GOCI), launched in 2010. To ensure the reliability of GOCI-II data for practical applications, the accuracy of remote sensing products needs to be validated. This study uses in situ data from Lake Taihu for validation.MethodsWe assessed the accuracy of GOCI-II remote sensing products, including remote sensing reflectance derived using two atmospheric correction algorithms: ultraviolet (UV) and near-infrared (NIR). The study also evaluated the accuracy of derived parameters, such as chlorophyll-a (Chl-a) concentration, total suspended matter (TSM) concentration, and phytoplankton absorption coefficient (aph), based on these atmospheric correction algorithms. In situ measurements from Lake Taihu were used as ground truth data for validation.ResultsOur results revealed that the UV atmospheric correction algorithm provided higher accuracy in Lake Taihu compared to the NIR algorithm. The average absolute percentage deviations (APDs) for remote sensing reflectance across different bands were: 25.17% (412 nm), 29.69% (443 nm), 22.27% (490 nm), 19.38% (555 nm), 36.83% (660 nm), and 33.0% (680 nm). Compared to NIR-derived products, the UV algorithm showed improved accuracy for Chl-a concentration, TSM concentration, and aph, with reductions in APD values by 16.92%, 3.32%, and 10.91%, respectively. When applying UV correction, the 412 nm band performed better than the 380 nm band, likely due to a lower signal-to-noise ratio at 380 nm and smaller extrapolation errors at 412 nm.DiscussionWhile the NIR algorithm is suitable for open ocean waters, the UV algorithm demonstrated higher accuracy in turbid environments such as Lake Taihu. Therefore, a combined UV-NIR atmospheric correction algorithm may be more effective for handling various types of water environments. Additionally, further research is needed to develop more suitable retrieval algorithms for Chl-a concentration and aph in eutrophic waters to improve accuracy.

Analysis of the Generation and Spatiotemporal Distributions of Dust During Tunnel Construction

The dust generated during tunnel construction poses serious health risks to workers, as it not only causes respiratory obstruction but also leads to pneumoconiosis and respiratory failure after prolonged exposure. However, most existing studies focus on specific construction stages or particular particle sizes and often assume an ideal airflow, neglecting the complex flow fields, vortex effects, and dust composition variations at different stages in tunnel and mine construction. This study systematically analyzes the spatiotemporal distribution characteristics of dust at various stages of tunnel construction and proposes targeted prevention and control strategies. On the basis of measured data from three construction stages—the working face, initial support, and secondary lining stages—and SPSS 27 statistical analysis, a dynamic analysis was conducted on the concentration and distribution patterns of total suspended particulates (TSPs) and particulate matter of different sizes (PM10, PM4, PM2.5, and PM1). The results show that coarse particles dominate during the working face stage, whereas fine particles gradually accumulate during the initial support and secondary lining stages. Finally, this work establishes a dust concentration–excavation time/tunnel depth equation and proposes targeted dust control measures. These findings offer important practical value for enhancing construction safety and air quality.

Adaptation of Satellite Remote Sensing Algorithms for Retrieval of Environmental Parameters in the Caspian Sea

The state of the Caspian Sea ecosystems causes serious concern due to anthropogenic (oil and gas production and transportation) and natural reasons (climatic changes, extreme meteorological and hydrological phenomena, natural hazards and their frequency of recurrence, etc.). Climatic changes have already led to a sharp drop in the Caspian Sea level, an increase in sea surface temperature, changes in ice conditions in the Northern Caspian, the intensity of phytoplankton blooms, especially in the Southern Caspian, an increase in turbidity in coastal areas due to river runoff, and changes in the frequency and intensity of wind patterns. As a rule, such phenomena have a significant specific (more often negative) impact on the natural systems and infrastructure of the coastal zone. To understand the current state and climatic changes in the main parameters of the marine environment in the Caspian Sea and to carry out its comprehensive analysis, it is necessary to use remote sensing data and to determine correctly different environmental parameters. Our experience shows that in many cases standard algorithms for retrieval of these parameters from the remote sensing data that are elaborated, calibrated and validated for the open ocean conditions, are not valid for inland seas which have their own specific physico-geographical, oceanographic and meteorological conditions. The paper presents a review of new algorithms and techniques developed during the past decade for correct retrieval of sea surface temperature, water turbidity, suspended particulate matter concentration, as well as determination of ice cover, river plumes, and wind surge areas based first of all on Ocean Color Data: MSI Sentinel-2A, -2B; OLI/TIRS Landsat-8 and OLI-2/TIRS-2 Landsat-9. These algorithms have been introduced into the specialized information and analytical system “See the Caspian Sea” (STCS), designed for collecting, analyzing and visualizing satellite and meteorological data for the Caspian Sea region.

Assessment of PM2.5 pollution features and health advantages in Northwest China.

The elevated concentration of suspended particulate matter in the atmosphere is closely associated with the morbidity and mortality of human diseases. In northwest China, fine particulate matter pollution has persistently affected local health, production, and daily life due to climatic factors and economic development. This paper first examines the distribution and variation of PM2.5 concentrations in northwest China from 2013 to 2022 to understand changes in PM2.5 exposure levels, as well as their associated health and economic benefits since the implementation of the "Air Pollution Prevention and Control Action Plan." Second, we estimated the number of premature deaths in the research region under various control scenarios based on PM2.5 concentrations recorded in 2013 and 2021, utilizing existing epidemiological studies for reference. Finally, we calculated the financial benefits resulting from variations in PM2.5 concentrations within this region. Results indicated that (1) from 2013 to 2022, the Tarim Basin exhibited the highest PM2.5 concentration among all studied areas, while northern Xinjiang and Qinghai Province had notably lower levels. Additionally, wintertime concentrations were consistently higher than those observed during summer months; however, there has been a general trend toward reduced PM2.5 content over the past decade. (2) A significant inverse relationship was found between premature mortality rates and PM2.5 concentrations; specifically, fluctuations in PM2.5 levels exerted a more pronounced impact on premature deaths attributable to cardiovascular diseases compared to respiratory conditions within this northwest region. (3) Males were disproportionately affected by increases in PM2.5 concentration relative to females-experiencing two to three times more early fatalities than their female counterparts did. (4) Over a 9-year period within this research area, fiscal benefits derived from interventions targeting PM2.5 treatment increased by approximately 891 million yuan.

Evaluation of SDGSAT-1 MII data in total suspended matter estimation in clear to extremely turbid rivers

ABSTRACT The Multispectral Imager (MII) onboard the newly launched SDGSAT-1 holds significant promise for monitoring and understanding variations in water quality within nearshore water systems, with an impressive spatial resolution of 10 m, seven spectral bands in the visible and near-infrared domain and a wide swath of 300 km. This paper evaluates the MII data in estimating the concentration of total suspended matter (TSM) in rivers and estuaries from clear to extremely turbid conditions. For atmospheric correction, the ACOLITE model showed superior performance compared to the FLAASH and 6SV, with average unbiased relative error (AURE) ranging from 11.8% to 26.4% in the four visible bands of MII. Through comparison, a TSM model based on the visible band ratio (i.e., Rrs (660)/Rrs (560)) was proposed to ensure the application of MII data in both clear and turbid waters, with an overall AURE of 40.9%. The model was then applied to map the TSM variations in the Hai and Liao Rivers in northern China using the MII data, where reliable spatial and temporal patterns were observed. The study concludes by discussing the applicability of MII data in water quality mapping in nearshore waters, along with key considerations such as model applicability, atmospheric correction, and land adjacency.

Variability of inherent optical properties of seawater in relation to the concentration and composition of suspended particulate matter in the coastal Arctic waters of western Spitsbergen

Inherent optical properties (IOPs) and characteristics of suspended particles in surface seawater samples were measured in summer months of 2021 and 2022 in Arctic fjords and coastal waters of western Spitsbergen in the Svalbard archipelago. The measured IOPs included the spectral backscattering and scattering coefficients of suspended particulate matter, as well as the spectral absorption coefficients of suspended particulate matter and its non-algal particulate and phytoplankton components. The particulate assemblages were characterized by measuring the mass concentrations of suspended particulate matter (SPM), particulate organic matter (POM), particulate inorganic matter (PIM), and phytoplankton pigments including chlorophyll-a (Chla). The investigated coastal waters exhibit high variability of particulate characteristics and associated IOPs. We observed more than two orders of magnitude variation in SPM and particulate IOPs, and Chla varied from below the detection limit to more than 3 mg m−3. The contribution of organic fraction to SPM (POM/SPM ratio) varied from 0.05 to 0.6, and the Chla/SPM ratio spanned more than three orders of magnitude with a maximum value of the order of 10−3. As a result, the mass-specific optical coefficients, especially the mass-specific backscattering and scattering coefficients of particles and mass-specific absorption coefficient of non-algal particulate matter, exhibit large variations. In addition, our study demonstrates the influences of changes in composition of suspended particulate matter parameterized in terms of POM/SPM and Chla/SPM ratios on IOPs. Various variants of spectral parametrizations of optical coefficients in terms of univariate or multivariable relationships with particulate characteristics are provided. These parameterizations are representative of the investigated coastal Arctic waters in Svalbard region and can be used for better interpretation of optical measurements, both in-situ and remote, in this Arctic environment.

Estimating Vertical Distribution of Total Suspended Matter in Coastal Waters Using Remote-Sensing Approaches

The vertical distribution of the marine total suspended matter (TSM) concentration significantly influences marine material transport, sedimentation processes, and biogeochemical cycles. Traditional field observations are constrained by limited spatial and temporal coverage, necessitating the use of remote-sensing technology to comprehensively understand TSM variations over extensive areas and periods. This study proposes a remote-sensing approach to estimate the vertical distribution of TSM concentrations using MODIS satellite data, with the Bohai Sea and Yellow Sea (BSYS) as a case study. Extensive field measurements across various hydrological conditions and seasons enabled accurate reconstruction of in situ TSM vertical distributions from bio-optical parameters, including the attenuation coefficient, particle backscattering coefficient, particle size, and number concentration, achieving a determination coefficient of 0.90 and a mean absolute percentage error of 26.5%. In situ measurements revealed two distinct TSM vertical profile types (vertically uniform and increasing) and significant variation in TSM profiles in the BSYS. Using surface TSM concentrations, wind speed, and water depth, we developed and validated a remote-sensing approach to classify TSM vertical profile types, achieving an accuracy of 84.3%. Combining this classification with a layer-to-layer regression model, we successfully estimated TSM vertical profiles from MODIS observation. Long-term MODIS product analysis revealed significant spatiotemporal variations in TSM vertical distributions and column-integrated TSM concentrations, particularly in nearshore regions. These findings provide valuable insights for studying marine sedimentation and biological processes and offer a reference for the remote-sensing estimation of the TSM vertical distribution in other marine regions.

Retrieval of total suspended matter concentration in the yellow river estuary offshore area based on QAA-RF model

ABSTRACT Total suspended matter is one of the crucial water quality parameters for both inland and marine environments, and a key role in evaluating the water quality of estuaries and offshore areas. Each year, the Yellow River carries a significant amount of sediment into the semi-enclosed Bohai Sea, results in a prolonged high concentration of total suspended matter in the offshore areas of the Yellow River Estuary. This study focuses on the offshore region of the Yellow River Estuary in China. Utilizing Sentinel-2 satellite imagery data from 2020 to 2023 and in-situ measured data from August 2020 to August 2022, to address the lack of physical mechanisms currently studied in machine learning retrieval methods, a model that integrates the physics-driven Quasi-Analytical Algorithm (QAA) and data-driven Random Forest (RF) is employed for the retrieval of total suspended matter concentration in the study area. The fused model (QAA-RF) is compared and analysed against regression models and standalone machine learning models. The results indicate that the accuracy of machine learning retrieval models is consistently higher than that regression models. The QAA-RF model demonstrates the highest accuracy (R 2 = 0.87, MAE = 5.01 mg L−1, RMSE = 6.39 mg L−1). Based on the QAA-RF model and the imagery data, monthly total suspended matter concentration is conducted in the study area. The retrieval results indicates that: (1) the total suspended matter concentrations is primarily concentrated in the near estuary region, with concentrations decreasing as distance from the estuary increases. (2) the total suspended matter concentration exhibits a distribution pattern with higher values in spring and winter, and lower values in summer and autumn. (3) the concentration of total suspended matter shows relatively small fluctuations at the annual scale from 2020 to 2023.

Multi-Scale Variation in Surface Water Area in the Yellow River Basin (1991–2023) Based on Suspended Particulate Matter Concentration and Water Indexes

Surface water is a crucial part of terrestrial ecosystems and is crucial to maintaining ecosystem health, ensuring social stability, and promoting high-quality regional economic development. The surface water in the Yellow River Basin (YRB) has a high sediment content and spatially heterogeneous sediment distribution, presenting a significant challenge for surface water extraction. In this study, we first analyze the applicability of nine water indexes in the YRB by using the Landsat series images (Landsat 5, 7, 8) and then examine the correlation between the accuracy of the water indexes and suspended particulate matter (SPM) concentrations. On this basis, we propose a surface water extraction method considering the SPM concentrations (SWE-CSPM). Finally, we examine the dynamic variations in the surface water in the YRB at four scales: the global scale, the secondary water resource zoning scale, the provincial scale, and the typical water scale. The results indicate that (1) among the nine water indexes, the MBWI has the highest water extraction accuracy, followed by the AWEInsh and WI2021, while the NDWI has the lowest. (2) Compared with the nine water indexes and the multi-index water extraction rule method (MIWER), the SWE-CSPM can effectively reduce the commission errors of surface water extraction, and the water extraction accuracy is the highest (overall accuracy 95.44%, kappa coefficient 90.62%). (3) At the global scale, the maximum water area of the YRB shows a decreasing trend, but the change amount is small. The permanent water area shows an uptrend, whereas the seasonal water area shows a downtrend year by year. The reason may be that the increase in surface runoff and the construction of reservoir projects have led to the transformation of some seasonal water into permanent water. (4) At the secondary water resource zoning scale, the permanent water area of other secondary water resource zonings shows an increasing trend in different degrees, except for the Interior Drainage Area. (5) At the provincial scale, the permanent water area of all provinces shows an uptrend, while the seasonal water areas show a fluctuating downtrend. The maximum water area of Shandong, Inner Mongolia Autonomous Region, and Qinghai increases slowly, while the other provinces show a decreasing trend. (6) At the typical water scale, there are significant differences in the water area variation process in Zhaling Lake, Eling Lake, Wuliangsuhai, Hongjiannao, and Dongping Lake, but the permanent water area and maximum water area of these waters have increased over the past decade. This study offers significant technical support for the dynamic monitoring of surface water and helps to deeply understand the spatiotemporal variations in surface water in the YRB.

Spatial and Temporal Variations of Total Suspended Matter Concentration during the Dry Season in Dongting Lake in the Past 35 Years

Dongting Lake is the second largest freshwater lake in China, located in the middle reaches of the Yangtze River. Since the 21st century, it has faced intensified human activities, particularly the Three Gorges Dam impoundment and sand mining. The water quality of Dongting Lake has significantly changed due to human activities and climate change. Currently, quantitative studies on the spatial–temporal variations of total suspended matter (TSM) during Dongting Lake’s dry season and the human impacts on its concentration are lacking. This study utilizes Landsat-5 TM and Landsat-8 OLI data to estimate the changes in TSM concentration during the dry season from 1986 to 2021, analyzing their spatial–temporal variations and driving mechanisms. By evaluating the atmospheric calibration accuracy and model precision metrics, we select a model based on the ratio of red to green band, achieving an R2 of 0.84, RMSE of 18.94 mg/L, and MRE of 27.32%. Applying this model to the images, we map the distribution of the TSM concentration during the dry season from 1986 to 2021, analyzing its spatial pattern and inter-annual variation, and further investigate the impacts of natural factors and human activities on the TSM concentration. Our results show the following: (1) From 1986 to 2021, the TSM concentration during the dry season ranges from 0 to 200 mg/L of Dongting Lake, with an area-wide average value between 41.61 and 75.44 mg/L. (2) The TSM concentration from 1986 to 2021 is significantly correlated with the water level. Before 2006, it correlates positively, but no significant correlation exists from 2006 onward. (3) From 2006 onward, the mean TSM concentration is notably decreased compared to that before 2006, likely due to the Three Gorges Dam, while our analysis indicates a significant positive correlation between the TSM concentration and sand mining intensity during this period. This study highlights the influence of the Three Gorges Dam and sand mining on the TSM concentration in Dongting Lake during the dry season, providing valuable insights for related research on similar lakes.

Effects of variations in hydrological connectivity on the macrobenthic community structure in reclaimed wetlands

The Nanhui Dongtan Wetland is the most extensively reclaimed part of the Yangtze River Estuary wetland. In recent decades, urbanization has led to the extensive reclamation of the intertidal wetlands of Nanhui Dongtan. Macrobenthos are crucial as secondary production groups in the food web. However, there is a lack of in-depth research on the response mechanisms of macrobenthic communities to environmental disturbances in reclaimed wetlands. This study investigated the impact of hydrological connectivity changes caused by land reclamation on the macrobenthic community based on the macrobenthic community composition in preserved tidal flats and closed and open reclamation areas in Nanhui Dongtan. The results showed that the macrobenthos species richness in the closed reclamation area was significantly lower than that in the other areas. After dividing the functional groups of macrobenthos, structural equation modeling revealed a negative correlation between the salinity and the functional group composition. Analysis of the food sources revealed significant positive correlations between predatory and sediment-feeding populations and sediment organic matter content, between detritivorous group and environmental chlorophyll-a content, and between herbivorous group and suspended organic matter content in water. Therefore, variations in hydrological connectivity in different reclamation areas caused differences in food source distribution, which led to different compositions of macrobenthic functional groups. The results of this study provide a theoretical reference for the study of intertidal wetland habitat restoration.

Source apportionment of PM2.5 episodes in the Taichung metropolitan area, Taiwan

To analyze the physicochemical mechanisms affecting the variation in fine suspended particulate matter (PM2.5) concentrations in Taichung City, the largest city in central Taiwan (the second largest city in Taiwan), during a high-pollution event from November 3 to 6, 2021, we applied the sulfur tracking method (STM) and integrated source apportionment method (ISAM) of the WRF/CMAQ model to simulate the impacts of various emission sources. The sources of pollution in Taichung City are very similar, which shows that the impacts of point, line, and area sources should not be neglected in addition to the boundary conditions. SO42− is mainly generated from point emissions and the production of H2O2, Fe, Mn, and O3. NO3− is also mainly generated from point sources in Taichung City, with HNO3 being the main source at noon and ANO3 at other times of the day. NH4+ is mainly generated from area sources in Taichung City. OM is more complex, mainly originating from line sources in Taichung City and other sources, such as point/area emissions in Taichung City and other emissions from Changhua County. The most important mechanism is low-volatility/semivolatile oxidized combustion of OC at noon, followed by low-volatility/semivolatile POA, which is produced in the morning or evening. EC mainly originates from line sources in Taichung City and Changhua County. In other nearby counties, EC is dominated by local emission sources. In addition, when the concentration of PM2.5 is high, the Neutralization Ratio (NR) is high and PM2.5 is relatively neutral or slightly alkaline. On the contrary, when the concentration of PM2.5 is low, the NR is lower than 1 and the aerosol is acidic. Besides, this study used positive matrix factorization (PMF), which indicates that the PM2.5 at the UAPRS originated from eight kinds of pollution, namely, windblown dust, oil cracking, iron and steel industry, sea salt, transport, Cl− containing exhaust, biomass burning, fossil combustion containing abundant SO42− and the heavy oil refining and coal combustion industry. The direction of the source of pollution can be traced by a conditional bivariate probability function (CBPF). Overall, fossil fuel combustion, mainly involving sulfate, is the largest source of pollution, with the heavy oil refining and coal combustion industry contributing less, and the remaining factors contribute relatively evenly.

Unveiling the effects of environmental factors and Yellow River inputs on the ichthyoplankton community structure in typical bays

To unravel the effects of environmental factors on fishery resources in the bay, we conducted six biological and environmental surveys in the Laizhou Bay between 2013 and 2020. The findings of our study illuminated several key aspects: (1) The annual discharge of water and sediment from the Yellow River to Laizhou Bay exhibited notable variations, while concurrently, environmental factors including temperature, salinity, and suspended particle matter underwent fluctuations, yet remained within a relatively stable range overall. (2) A total of 8318 eggs and larvae belonging to 10 orders, 16 families, and 19 genera were collected. Significant interannual fluctuations had been documented in the species composition, abundance, and biodiversity of ichthyoplankton. Notably, both Shannon-Wiener diversity index and Pielou evenness index were significantly negatively correlated with suspended particle matter concentration. (3) The water and sediment discharge significantly positively correlated with the number of cold-temperature species. However, the sediment input negatively correlated with the number of continental shelf benthopelagic fish. (4) Redundancy and correlation analyses confirmed the strong link between spatial and temporal distribution of fish communities and environmental factors, with salinity and dissolved oxygen key for ichthyoplankton abundance. Our research offers a scientific foundation for targeted fishery protection and management, which is crucial for preserving the ecological functions of spawning grounds in the bay.

Remote sensing of suspended particulate matter concentrations in the Yellow River Estuary, China: algorithm development, long-term dynamics and driving mechanisms

Remote sensing of suspended particulate matter concentrations in the Yellow River Estuary, China: algorithm development, long-term dynamics and driving mechanisms

Validation of the method for determining priority phthalates by GC-MS at trace concentrations in surface water with a background pollution level

A methodology for determining priority phthalates (PP) at trace concentrations in surface waters with a background pollution level has been validated. Lake Baikal, the largest natural reservoir that retains up to 20% of the world’s fresh surface waters, was chosen as a natural model for the study. Baikal is characterized by a minimal content of suspended organic matter, a low degree of mineralization and a background level of organic pollutants. Four priority phthalates were found in Baikal water: dimethyl phthalate, diethyl phthalate di-n-butyl phthalate and di-(2-ethylhexyl) phthalate present in the concentration range from 0.01 to 0.66 μg/liter. The method for phthalate determination includes a single liquid-liquid extraction of phthalates (Vsample = 1 liter) and direct analysis of the extracts by GC-MS. Considering the minimum content of suspended particles in water and the use of high sensitivity of the mass spectrometry, the stages of sample filtration and concentration of extracts are excluded from the procedure. Deuterated phthalates are used as surrogate internal standards for the quantitative determination of priority phthalates. The laboratory background of phthalates was assessed using reagent-blank methods, exhaustive multiple extraction and subsequent exclusion of the resulting value from the determination result as a systematic error. The limits of phthalate determination (0.01 – 0.17 μg/liter) and the error of determination (±δ from 12 to 38%) were assessed in the concentration range from 0.01 to 0.66 μg/liter. The method was validated during monitoring of persistent organic pollutants in water of Lake Baikal for the period 2015 – 2023.

Retrieval and spatiotemporal variation of total suspended matter concentration using a MODIS-derived hue angle in the coastal waters of Qinhuangdao, China

Retrieval and spatiotemporal variation of total suspended matter concentration using a MODIS-derived hue angle in the coastal waters of Qinhuangdao, China

Timescales of Ecological Processes, Settling, and Estuarine Transport to Create Estuarine Turbidity Maxima: An Application of the Peter–Parker Model

The estuarine exchange flow increases the longitudinal dispersion of passive tracers and trap sinking particles, potentially creating an estuarine turbidity maximum (ETM): a localized maximum of suspended particulate matter concentration in an estuary. The ETM can have many implications: dead zones due to increased turbidity or hypoxia from organic matter decomposition, naval navigation challenges, and other water quality problems. Using timescales, we investigate how the interaction between exchange flow and particle sinking leads to ETMs by modeling a sinking tracer in an idealized box model of the Total Exchange Flow (TEF) first developed by Parker MacCready. Results indicate that the balance of particle sinking and vertical mixing is critical to determining ETM size and location. We then focus on the role of ecology in ETM formation through the use of the Peter–Parker Model, a new biophysical model which combines the TEF box model with a Nutrient–Phytoplankton–Zooplankton–Detritus (NPZD) model, the likes of which were first developed by Peter J.S. Franks. Detritus sinking rates similarly influence detritus peak concentration and location (an ETM), but detritus ETMs occur in a different location than the sinking tracer due to the influence of biological factors, which create a time lag of about 1 day. Lastly, we characterize the flow of the models with a dimensionless parameter that compares timescales and summarizes the dynamics of the sinking tracer in ETM formation and that can be used across systems.

Modern Methods and Technical Instruments of Ecological Monitoring of the Estuaries of Small Rivers

At present, river estuaries are constantly being explored; therefore, it is necessary to receive up-todate information on their actual environmental condition, hydrological regime, and geographical features. Therefore, it is important to develop and improve the methods and technical instruments for monitoring river estuaries. The article presents a developed methodology for integrated research of small-river estuaries using new methods and techniques. The article discusses the experience in using modern domestic devices for solving practical problems of integrated water-environmental monitoring with a case study of expeditionary work in estuaries of the Chernaya River (Crimean Peninsula, Sevastopol). The possibilities of the following technical instruments used are described: The GAP-AK-12R CTD (MHI RAS), the Condor biophysical complex (Aquastandard), and the Garmin echo sounder, which make it possible to obtain field data on turbidity, the concentration of suspended particulate matter, speed and direction of currents, the average size of suspended particles, and salinity and temperature of the aquatic environment. The results of applying a new method for determining the flow velocity and water consumption based on video image processing are presented. Application of the developed methodology makes it possible to obtain the information necessary to analyze the hydrological regime and ecological state of small-river estuaries.

Optimal ventilation strategies for balancing carbon dioxide and suspended particulate matter concentrations in vehicle cabins

Maintaining safe levels of carbon dioxide (CO2) and particulate matter (PM) in vehicle cabins during driving is essential to ensure driver safety and minimize health risks. However, while the fresh-air ventilation mode reduces the CO2 level, it also allows the ingress of PM pollutants from the external environment and hence increases the PM concentration in the cabin. Thus, effective ventilation strategies are required to optimize the air quality by balancing the average levels of CO2 and suspended PM within the cabin. Accordingly, this study performed computational fluid dynamics simulations to explore the effects of different air-conditioning ventilation modes on the indoor air quality in the cabin of a commercial sports utility vehicle. The simulations commenced by examining the optimal switching times between the recirculation and fresh-air modes that yielded an average CO2 concentration of less than 1000 ppm for various numbers of occupants in the cabin. Further simulations were then performed to investigate the effect of the air filtration efficiency of the in-cabin filter on the PM concentration in the cabin under the optimal ventilation strategies determined for 1–5 occupants. The results indicated that, as the number of occupants increased, the required fresh-air mode usage time also increased. Furthermore, to mitigate the corresponding increase in the PM concentration, a higher filtration efficiency was required. Overall, the results showed that the optimal switching cycles achieved the required indoor air quality within the cabin by successfully balancing the CO2 and suspended PM concentrations.