Anthropogenic Sources Research Articles

Frequencies of micronuclei in buccal cells and their spatial distribution in a population living in proximity to coal mining areas in southern Brazil

The extraction and burning of coal release genotoxic pollutants, and understanding the relationship between genetic damage and the spatial distribution of residences in coal-using regions is crucial. The study aimed to conduct a spatial analysis of genotoxic damage through the of micronuclei (MNs) number and their proximity to coal mining/burning in the largest coal exploration region in Brazil. In this study, the detection of genotoxic damage was performed using the MN assay in oral cells of residents exposed to coal mining activities. Spatial analysis was conducted using QGIS 3.28.10 based on information obtained from a questionnaire administered to the population. Multiple linear regression analysis was carried out to assess the influence of the distance from residential areas to polluting sources on the number of MNs found. Additionally, Spearman's correlation was performed to identify the strength and direction of the association between the frequency of MNs and each of the polluting sources. A total of 147 MNs were quantified among all participants in the coal mining region. Notably, residents living within 2 km and 10 km of pollution sources exhibited the highest prevalence of MNs. The analysis demonstrated a significant correlation between closer proximity to pollution sources and increased MN frequency, underscoring the spatial relationship between these sources and genotoxic damage. Environmental pollutants from anthropogenic sources present a major health risk, potentially leading to irreversible damage. The spatial analysis in this study highlights the importance of targeted public policies. These policies should aim for a sustainable balance between economic development and public health, promoting effective measures to mitigate environmental impacts and protect community health.

Potentially toxic metals contamination in stream sediments of Mbal area (Pan-African fold belt, Cameroon)

The Mbal area is situated within the Neoproterozoic Lom Basin in Cameroon. The objective of this paper was to determine the level of contamination and risk associated of potentially toxic metals (PTMs) in stream sediments of Mbal. Sampling was performed by panning and collected sediment were analysed using an inductively coupled plasma-mass spectrometer. The sediment samples showed significant enrichment in mercury, above 10000 ppb. The other transition metals including manganese, zinc, lead were also elevated, ranging from 825 to 1950, 50.80 to 97 and 9.80 to 87.20 ppm, respectively. Multivariate statistical analyses revealed that the enrichment of the PTMs were attributed both to geogenic and anthropogenic sources. Pollution indicators such as degree of contamination, modified degree of contamination and pollution load index yielding 10809.33–200005, 982.67–18182.33 and 0.59–1.18 respectively, suggest high degree of sediments contamination from natural and anthropogenic sources. Geo-accumulation index and enrichment factor revealed moderate to extreme levels of metallic pollution. Potential ecological risk indices show extreme ecological risk index of PTMs suggesting ecological effects to biota and humans in the ecosystem. Mercury, lithology, mining, domestic and agricultural activities were major sources of PTMs pollution. Toxic metals may cause chronic health problems such as cardiovascular diseases, nervous system disorder, kidney damage and gastrointestinal disorder. This study underlines the importance of continuous monitoring and adaptive management for the preservation of the biotic and non-biotic environment. The results may assist mining companies and local populations in setting realistic targets for monitoring and remediation programs.

Follow the Trace: Becoming a Seismo-Detective with a Campus-Based Raspberry Shake Seismometer

Abstract Seismic signals, whether caused by earthquakes, other natural phenomena, or artificial noise sources, have specific characteristics in the time and frequency domains that contain crucial information reflecting their source. The analysis of seismic time series is an essential part of every seismology-oriented study program. Enabling students to work with data collected from their own campus, including signals from both anthropogenic and natural seismic sources, can provide vivid, practical examples to make abstract concepts communicated in classes more concrete and relevant. Data from research-grade broadband seismometers enable us to record time series of vibrations at a broad range of frequencies; however, these sensors are costly and are often deployed in remote places. Participation in the Raspberry Shake citizen science network enables seismology educators to record seismic signals on our own campuses and use these recordings in our classrooms and for public outreach. Yale University installed a Raspberry Shake three-component, low-cost seismometer in the Earth and Planetary Sciences department building in Summer 2022, enabling the detection of local, regional, and teleseismic earthquakes, microseismic noise, and anthropogenic noise sources from building construction, an explosive event in a steam tunnel, and general building use. Here, we discuss and illustrate the use of data from our Raspberry Shake in outreach and education activities at Yale. In particular, we highlight a series of ObsPy-based exercises that will be used in courses taught in our department, including our upper-level Introduction to Seismology course and our undergraduate classes on Natural Disasters and Forensic Geoscience.

Low-cost UAV coordinated carbon observation network: Carbon dioxide measurement with multiple UAVs

Improving the mitigation of global greenhouse effects requires drastic reductions in carbon emissions. Effective carbon management at different spatial scales is therefore crucial to a more sustainable path of economic development. Regional and local carbon management currently rely on the traditional bottom-up method of emission reports, which are known for their opaque and complex procedures. Novel top-down carbon monitoring methods including space-based, aerial and ground-based observations are tantalising alternatives to more reliable data sources of carbon emissions as well as sinks. Unmanned Aerial Vehicles (UAVs) based carbon monitoring is an upcoming field because of its lower costs, higher spatial-temporal resolution and operability at a wider range of weather conditions compared to satellite observations. UAVs equipped with Non-dispersive Infrared (NDIR) carbon dioxide (CO2) sensors can measure in the air with faster response and can intelligently navigate themselves within areas of greater interest. With the help of lighter NDIR payloads and multiple UAVs flying in synchrony, larger cross sections or areas for maximising information acquisition of emission plumes can be covered. In this article, we first introduce the design of a miniaturised NDIR payload, which is compatible with a lightweight UAV to achieve a single system weighing less than 4 kg. Then, we test our design via comparisons against another NDIR payload to determine its accuracy (−0.82 ppm and −0.44 ppm) and precision (0.12 ppm and −0.03 ppm) in measurements of two vertical concentration profiles. Finally, we deployed four UAVs on two major anthropogenic emission source monitoring campaigns with coordinated flight patterns. To fully understand the benefit of multi-UAV carbon monitoring, we attempted two flight patterns and computed power plant emission rates via the cross-sectional flux method. While all flights successfully captured the concentration enhancements downwind of the power plants, the single-sectional flight pattern measured an emission rate of 10ktCO2/day which is 44% less than that reported monthly and the multi-sectional flight pattern yielded relatively varied results (8.5ktCO2/day on average, which is 54% less) for the respective sections due to wind underestimations, plume section undersampling and wind fluctuations.

Unveiling Unprecedented Methane Hotspots in China's Leading Coal Production Hub: A Satellite Mapping Revelation

AbstractChina is likely the world's largest anthropogenic source of methane emissions, with coal mine methane (CMM) being the predominant contributor. Here, we deploy 2 years of satellite observations to survey facility‐level CMM emitters in Shanxi, the most prolific coal mining province in China. A total of 138 detected episodic events at 82 facilities are estimated to emit 1.20 (+0.24/−0.20, 95% CI) million tons of methane per year (Mt CH4/yr) during 2021–2023, roughly equivalent to 4.2 times the integrated flux from the Permian plumes and four times of the integrated flux from the Four Corners plumes, two of the world's largest hotspots for oil and gas methane emissions. This work reveals the heavy‐tailed distribution characteristic of CMM emission sources for the first time, with 20% of emitters contributing approximately 50% of total emissions. Comparison with the Global Energy Monitor (GEM) inventory reveals that the GEM estimate is about 4.1 times our estimate.

Log-Linear Model and Delivery Load Analysis for Improvements in Water Quality through TMDL in the Gyeongan Stream Watershed, Republic of Korea

The effectiveness of implementing the total maximum daily load (TMDL) in the Gyeongan stream watershed was evaluated to assess its impact on water quality. The relationships between water quality and flow rate and load and water quality were reinterpreted using a log-linear model and the delivery load, respectively. To estimate annual water quality trends and analyze the effects of water quality improvement in each parameter, an improved multivariable log-linear model addressing the limitations of the traditional L-Q equation was applied to analyze the relationship between water quality and flow rate, excluding the effects of flow rate and seasonality. The effect of total phosphorus (T-P) was particularly prominent. A new delivery load was developed and applied to address the limitations of the original unit-based delivery load. Evaluations showed that water quality continuously improved across all parameters, and all methods (excluding the influence of flow rate on water quality fluctuations) were highly effective in estimating water quality changes attributable to anthropogenic pollution sources. The analysis of pollutant contributions revealed that managing point sources is necessary for controlling the biochemical oxygen demand and total nitrogen, while point and non-point sources require T-P management. Future policy development should consider this when formulating management strategies.

Seasonal variability of volatile organic compounds (VOCs) at a high-altitude station in the Western Ghats, India: Influence of biogenic, anthropogenic emissions and long-range transport

Seasonal variability of volatile organic compounds (VOCs) was studied using year-long observations (June 2019–May 2020) over a high-altitude (1380m AMSL) forested site in the Western Ghats of India. Isoprene, a well-known biogenic VOC, peaked during pre-monsoon. Temperature stress and higher photo synthetically active radiation (PAR) were the drivers behind isoprene emissions from the surrounding forested regions. The daytime average isoprene mixing ratio was 1.92 ± 1.51 ppb during summer and the night time average was 0.02 ± 0.01 ppb. A strong relationship between isoprene mixing ratio and ambient temperature was derived. Most of the anthropogenic VOCs like acetonitrile, benzene, toluene and xylene were found to be higher during winter. These anthropogenic VOCs mostly exhibited afternoon to evening peaks, which suggests the contribution by traffic emissions along with other combustion sources (biofuels) and vertical updraft of pollutants from the surrounding valley region. The toluene to benzene ratio (T/B) was >1 during the afternoon and evening hours in monsoon, whereas, aged air masses with lower T/B ratios (<0.7) prevailed during other seasons. Polar bi-variate plots reveal that toluene, xylene and tri-methyl benzene (TMB) were mostly emitted at the south-west of the measurement site, irrespective of the season, indicating a dominant local traffic related source throughout the year. The contribution of local emissions and regional background on two selected aromatic VOCs, toluene and xylene was estimated, revealing the dominance of local anthropogenic sources during monsoon and post monsoon. Positive matrix factorization revealed three source factors in all the seasons with one additional factor during winter and pre-monsoon. The first factor was constituted of toluene, benzene, xylene and TMB, and the T/B ratio was ∼1.2, indicating traffic/fossil fuel contribution. A second ‘biomass burning’ factor was dominated by acetonitrile, ethanal, acetone, and benzene in which the T/B ratio was 0.45. Factors one & two were further validated by comparison with chamber experiments conducted using different combustion sources and solvents. The third factor was dominated by isoprene and monoterpenes which were attributed to biogenic emissions. The fourth factor comprised mainly of acetonitrile, ethanal, and acetone indicating aged air masses due to long-range transport with contributions from photochemical production and biomass burning.

A Modified Model for Quantitative Heavy Metal Source Apportionment and Pollution Pathway Identification.

Current source apportionment models have successfully identified emission sources and quantified their contributions. However, when being utilized for heavy metal source apportion in soil, their accuracy needs to be improved, regarding migration patterns. Therefore, this work intended to improve the pre-existing principal component analysis and multiple linear regression with distance (PCA-MLRD) model to effectively locate pollution pathways (traffic emissions, irrigation water, atmospheric depositions, etc.) and achieve a more precise quantification. The dataset of soil heavy metals was collected from a typical area in the Chang-Zhu-Tan region, Hunan, China in 2021. The identification of the contribution of soil parent material was accomplished through enrichment factors and crustal reference elements. Meanwhile, the anthropogenic emission was identified with principal component analysis and GeoDetector. GeoDetector was used to accurately point to the pollution source from a spatial differentiation perspective. Subsequently, the pollution pathways linked to the identified sources were determined. Non-metal manufacturing factories were found to be significant anthropogenic sources of local soil contamination, mainly through rivers and atmospheric deposition. Furthermore, the influence of irrigation water on heavy metals showed a more pronounced effect within a distance of 1000 m, became weaker after that, and then gradually disappeared. This model may offer improved technical guidance for practical production and the management of soil heavy metal contamination.

Geochemical indices for Zn, Cu, and Co in sediment cores of Coata River, Puno Region, Peru

Urban contamination resulting from the dynamics of unplanned cities has generated interest in recent decades due to increased pollutant emissions into the natural environment. The Coata River is a complex ecosystem exposed to pollution due to the high urbanization degree and consequent industrial dynamics, as well mining activities. Significant amounts of waste and pollutant substances are discharged into the river, often without any treatment, contributing to a drastic increase in the concentration of heavy metals that cause impacts and pressures on the environment. In this context, the present study contributes to the evaluation of the metal concentration (Zn, Cu, and Co) in three short sediment cores sampled at Coata River, Puno region, Peru. The acquired concentration data was compared with guideline reference values established by international sediment quality standards and used for calculations of the geochemical indices Enrichment Factor and Geoaccumulation Index. The highest concentrations were found for zinc (639 mg/kg) and copper (96 mg/kg), whilst zinc exhibited some values above the guidelines corresponding to the probable effect level (PEL, legislation of Canada) and mean range effects (ER-M, legislation of the United States). According to the sediment quality indices, the three stations range from moderately to highly contaminated with Zn, while Cu and Co show no contamination and moderate contamination, originating from both geogenic and anthropogenic sources.

Globally-significant arsenic release by wildfires in a mining-impacted boreal landscape

Metal mining and smelting activities are one of the largest anthropogenic sources of arsenic pollution to the environment, with pervasive consequences to human and environmental health. Several decades of metal processing activities near Yellowknife, NT, Canada have resulted in widespread accumulation of arsenic in biomass, soils, and sediments, exceeding environmental and human health limits. The landscape surrounding Yellowknife is frequently disturbed by wildfire, most recently in 2023, when 2500 km2 burned. While wildfire-mediated release of stored arsenic around Yellowknife likely represents an incipient threat to human and ecosystem health, a quantification of the potential magnitude of arsenic remobilization from wildfires is absent. Here we combine publicly available soil and biomass arsenic concentrations and land cover datasets with the current best estimates of pyrogenic arsenic speciation and release in upland and wetland ecosystems to estimate the potential range of arsenic remobilization due to wildfires in the region surrounding Yellowknife from 1972 to 2023. Since 1972, wildfires have potentially led to the release of 141–562 Mg of arsenic, with 61–381 Mg emitted to the atmosphere and 39–109 Mg mobilized as water-soluble species. The large range in potential atmospheric emissions was due to the range in peat emission efficiency (5%–84%) that resulted in more arsenic being released from wetlands than the uplands. In 2023 alone, our estimated atmospheric release from just four wildfires was between 15%–59% of global annual arsenic wildfire emissions and likely represented between 2 and 9% of total global arsenic emissions from all natural sources. Given that climate change has and will continue to increase both annual area burned and soil burn severity, we emphasize that future increased wildfire activity closer to Yellowknife will place legacy soil arsenic stores at risk of an even larger catastrophic and unprecedented release, especially as wetlands become drier.

Heavy Metals Levels and Contamination Status in Soils of Koudalwa, Tchad

This study is aimed at determining the heavy metal levels in agricultural soils and around oil drilling pits in Koudalwa, Tchad and evaluating pollution levels. Soil sampling was done on three sites AA (AA1 &amp;amp; AA2), BB (BB1 &amp;amp; BB2), CC (CC1 &amp;amp; CC2) with two samples collected per site, giving a total of six samples. The subsamples with code one represent the 0-30 cm depth and code two represent the 30-60 cm depth. A control sample was taken far from sites having any oil activities. 2 mm sieved soil samples were digested in a mixture of HCl and HNO&lt;sub&gt;3&lt;/sub&gt; in the ratio 1:3 and the concentrations of Cr, Cd, Pb, Hg, Cu, Mn, Fe and Ni in the digested solutions determined by atomic absorption spectroscopy. The contamination level of the heavy metals was assessed using the Geo-Accumulation Index (Igeo), Contamination factor, Pollution load index (PLI), Enrichment Factor (EF), and Potential Ecological Risk Assessment (PER) models. Results obtained shows that the abundance of heavy metals in the soils of Koudalwa is in the order; Cu ˃ Hg ˃ Cr ˃ Mn ˃ Fe ˃ Cd ˃ Ni ˃ Pb. The site AA shows the highest levels of all the determined heavy metals except Ni with highest amount of Cr (197.98 mg/kg), Cd (6.49 mg/kg), Hg (531.46 mg/kg), Ni (0.088 mg/kg) in the 30-60 cm soil depth, and Pb (0.021 mg/kg), Cu (815.24 mg/kg), Mn (166.55 mg/kg) and Fe (60.04 mg/kg) in the top soil, 0-30 cm depth. PLI analysis confirms that sites AA1 and AA2 present a decline of site quality as compared to other sites which are of perfect site quality. ER values for Cd, Cr, Cu, Hg showed severe enrichment due to contributions of anthropogenic sources with higher values in subsurface samples, thus indicating their high mobility in these soils. Results of this study shows that critical attention must be given to site AA as well as other activities in other sites that can contaminate the soils with special focus on remediation measures and sensitization of the population on the dangers of different activities on the soils.

Potential ecological and human health risk assessments of soil heavy metals in orchards of Antalya province in Türkiye

ABSTRACT Agricultural inputs containing heavy metals are used intensively in Türkiye. However, studies on the assessment of the effects of heavy metals on the environmental and human health risks of agricultural areas in Türkiye are very limited. The aim of this study was to determine the concentrations, sources and potential ecological and human health risks of heavy metals in soils from orchards in Antalya, Türkiye. A total of 127 soil samples were collected and analysed for cadmium (Cd), lead (Pb), nickel (Ni), chromium (Cr), cobalt (Co), iron (Fe), manganese (Mn), copper (Cu) and zinc (Zn). The mean concentrations of total Cd, Pb, Ni, Cr, Co, Fe, Mn, Cu and Zn were found to be 4.54, 64.68, 145.80, 94.00, 23.92, 28264, 763.30, 44.91, and 63.67 mg kg−1, respectively. The mean concentrations of Cd, Co, Ni, Pb, Cu and Mn of soils were higher than the European soil mean values. Enrichment factor (EF) and contamination factor (CF) values of soils indicate serious enrichment for Cd, Pb and Ni. According to the principal component analysis, Cd concentration in the soil from was anthropogenic sources, Ni, Cr and Pb from both anthropogenic and lithogenic origin while other heavy metals from natural sources. Total hazard index (THI) values of heavy metals were <1, suggesting that there were currently absent non-carcinogenic health risks to residents through ingestion, inhalation and dermal contact. Hazard index (HI) indicated that children (3.75) were sensitive 9.4 times more than adults (0.40) to non-carcinogenic risks of heavy metals. The carcinogenic risk values of Cd, Cr, Co and Ni were acceptable range and not expected carcinogenic risks.

Ecological risk and spatial distribution, sources of heavy metals in typical purple soils, southwest China

The identification and quantification of the ecological risks, sources and distribution of heavy metals in purple soils are essential for regional pollution control and management. In this study, geo-accumulation index (Igeo), enrichment factor (EF), pollution index (PI), potential ecological risk index (RI), principal component analysis (PCA) model and geographical detector (GD) were combined to evaluate the status, ecological risk, and sources of heavy metals (HMs) in soils from a typical purple soil areas of Sichuan province. The results showed that the average contents of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in purple soil were 7.77, 0.19, 69.5, 27.9, 0.077, 30.9, 26.5 mg/kg and 76.8 mg/kg, and the Igeo, EF and RI of topsoil Hg and Cd in designated area was the highest, and the average contents of Hg and Cd in topsoil were obviously greater than respective soil background value in Sichuan province and purple soil. The hot spots for the spatial distribution of 8 HMs were mainly focused in the southwest and northeast of the designated area, and there were also significant differences for 8 HMs distribution characteristics in the profile soil. Cu comes from both anthropogenic and natural sources, Zn, Ni and Cr mainly come from natural sources, but As, Pb, Hg and Cd mainly derived from human activities. GD results showed that soil texture (X18), altitude (X4), total nitrogen (TN), clay content (X3), sand content (X2) and silt content (X1) had the greatest explanatory power to 8 HMs spatial differentiation.This study provides a reference for understanding the status and influencing factors of HM pollution in typical purple soil, and lays a theoretical foundation for the environmental treatment of purple soil in China.

Machine Learning Classification of Air Quality Monitoring Stations to Achieve Ambient NO2 Objectives Using Emission Scenarios and Chemical Transport Model

Exceeding the latest Canadian Ambient Air Quality Standards (CAAQS) for NO2 concentration in Canadian cities motivates discussions on new policies and directives. Advanced air quality modeling and machine learning clustering algorithms integrating the Weather Research Forecast (WRF) and Community Multiscale Air Quality (CMAQ) models were used to classify air quality monitoring (AQM) stations based on pollution concentration modeling results. The sensitivity of ambient NO2 to the primary anthropogenic emission sources was investigated in Alberta, Canada. Emissions from two main sources, upstream oil and gas (UOG) and transportation sources, have been identified as the reason Alberta fails to meet the newly adopted NO2 CAAQS objectives. The air quality model was validated with ground-level observation data and the atmospheric model accurately replicates spatiotemporal NO2 variations. Despite contributing 62% of Alberta’s total NOx emissions, UOG influences ambient NO2 concentrations modestly in urban areas (<10%) but significantly affects rural regions. In contrast, transportation emission sources, responsible for 23% of NOx emissions, dominate ambient NO2 levels (up to 63%) in large cities. The discrepancy of emission contribution and ground-level concentrations, obtained from the chemical transport model, was resolved using a k-prototypes clustering algorithm to propose a new approach for categorizing AQM stations which led to improving the conventional classifications. The new approach considered the sensitivity of NO2 to emission reduction scenarios and provided an improved classification to be used for emission reduction interventions. Based on the updated classification, one set of AQM stations clearly showed sensitivity to NO2 emission reduction in the transportation sector despite their lower contributions to overall emissions. These stations were categorized as population exposure stations in large cities.

Chemical contamination affecting filter-feeding bivalves in no-take marine protected areas from Brazil

Marine protected areas (MPAs) are zones geographically delimited under pre-defined management goals, seeking to reduce anthropogenic threats to biodiversity. Despite this, in recent years reports of MPAs affected by chemical contamination has grown. Therefore, this study addresses this critical issue assessing legacy and current chemical contamination in filter-feeder bivalves obtained in very restrictive no-take MPAs from Brazil. The detected pollutants encompass polycyclic aromatic hydrocarbons (PAHs), linear alkylbenzenes (LABs), and persistent organic pollutants (POPs) like dichlorodiphenyltrichloroethane (DDTs) and polychlorinated biphenyls (PCBs). Despite protective measures, bivalves from nine MPAs exhibited high LABs (13.2–1139.0 ng g−1) and DDTs levels (0.1–62.3 ng g−1). PAHs were present in low concentrations (3.1–29.03 ng g−1), as PCBs (0.7–6.4 ng g−1), hexachlorobenzene (0.1–0.2 ng g−1), and Mirex (0.1–0.3 ng g−1). Regardless of the sentinel species, MPAs and management categories, similar accumulation patterns were observed for LABs, DDTs, PAHs, and PCBs. Based on the limits proposed by Oslo Paris Commission, the measured levels of PAHs, PCBs and were below the environmental assessment criteria. Such findings indicate the no biological effects are expected to occur. However, they are higher considering background conditions typically measured in remote or pristine areas and potential simultaneous exposure. Such findings indicate an influence of anthropogenic sources, emphasizing the urgency for monitoring programs guiding strategic management efforts to safeguard these areas.

Chemometric Analysis and Human Health Implications of Trace and Heavy/Non-Essential Metals through ingestion of Carbonated and Non-Carbonated Beverages.

Elevated levels of trace metals (TMs) and heavy/non-essential metals (HnMs) in commonly consumed beverages concern the public and regulatory agencies. Thus, frequent monitoring of these metals is critically important. The present study intended to assess TMs and HnMs concentrations and associated health risks in beverages. Ten metals, such as Mn, Co, Cr, Cu, and Zn (TMs) and Ni, Cd, Pb, Al, and As (HnMs), were quantified in different beverage brands categorized into two groups such as non-carbonated and carbonated beverages. Chemometric analysis such as hierarchical cluster analysis (HCA), Pearson's correlation coefficient (PCC), and principal component analysis (PCA) were also performed to demonstrate the possible natural and anthropogenic sources of metal contamination. Among the TMs, the mean concentration of Zn (233.3 ± 3.3-291.7 ± 3.2µg/L) followed by Mn (119.0 ± 2.3-146.4 ± 2.2µg/L) was found highest in both carbonated and non-carbonated beverage samples. In the case of HnMs, the lowest mean concentration of Cd (7.4 ± 0.9-18.6 ± 1.2µg/L) followed by Pb (4.1 ± 0.4-4.5 ± 0.4µg/L) was observed in both types of beverage samples. The tolerable dietary intake (TDI) value for Ni and provisional tolerable monthly intake (PTMI) value for Cd were higher than the value established by the WHO and EFSA. The computed values of the hazard index (HI < 1) and the cumulative cancer risk (CCR) indicated a low risk of exposure.

Phenomenology of the Composition of PM2.5 at an Urban Site in Northern France

In this work, PM2.5 was sampled at Dunkerque, a medium-sized city located in northern France. The mean concentration of PM2.5 during the sampling period was 12.6 ± 9.5 μg·m−3. Samples were analyzed for elemental and organic carbon (EC/OC), water-soluble organic carbon (WSOC), humic-like substances (HULIS-C), water-soluble inorganic ions, and major and trace elements. The origin and the variations of species concentrations were examined using elemental enrichment factors, bivariate polar plot representations, and diagnostic concentration ratios. Secondary inorganic ions were the most abundant species (36% of PM2.5), followed by OC (12.5% of PM2.5). Secondary organic carbon (SOC) concentrations were estimated to account for 52% of OC. A good correlation between SOC and WSOC indicated that secondary formation processes significantly contribute to the WSOC concentrations. HULIS-C also represents almost 50% of WSOC. The determination of diagnostic ratios revealed the influence of anthropogenic emission sources such as integrated steelworks and fuel oil combustion. The clustering of 72 h air masses backward trajectories data evidenced that higher concentrations of PM2.5, OC, and secondary inorganic aerosols were recorded when air masses came from north-eastern Europe and the French continental sector, showing the considerable impact of long-range transport on the air quality in northern France.

Noise source localization using deep learning

Summary Ambient noise source localization is of great significance for estimating seismic noise source distribution, understanding source mechanisms and imaging subsurface structures. The commonly used methods for source localization, such as the matched field processing and the full-waveform inversion, are time-consuming and not applicable for time-lapse monitoring of the noise source distribution. We propose an efficient alternative of using deep learning for noise source localization. In the neural network, the input data are noise cross-correlation functions and the output are matrices containing the information of noise source distribution. It is assumed that the subsurface structure is a horizontally layered earth model and the model parameters are known. A wavefield superposition method is employed to efficiently simulate ambient noise data with quantities of local noise sources labelled as training datasets. We use a weighted binary cross-entropy loss function to address the prediction inaccuracy caused by a sparse label matrix during training. The proposed deep learning framework is validated by synthetic tests and two field data examples. The successful applications to locate an anthropogenic noise source and a carbon dioxide (CO2) degassing area demonstrate the accuracy and efficiency of the proposed deep learning method for noise source localization, which has great potential for monitoring the changes of the noise source distribution in a survey area.

Assessment of radionuclides in the soil of Bonny Local Government Area Of Rivers State, Nigeria and evaluation of radiological risk

Twenty Samples each of soil from Bonny LGA, Rivers State, Nigeria were analyzed using NaI(IT) gamma detector to estimate radiation hazard due to the anthropogenic sources. The activity concentration of 232Th was found to be in the range 18.78 – 397.13 BqKg-1, 40K in the range 43.72 – 390.62 BqKg-1 and 226Ra in the range 10.77 – 57.84 BqKg-1 all in soil. These results were used to calculate the radiological hazard parameters including the Annual Gonadal Equivalent Dose. The calculated gamma exposure rates ranged between 10.00 – 270.79 nGyh-1 while the average value of the Excess Lifetime Cancer Risk (ELCR) was found to be 0.38 x 10-3 for soil which is higher than the world average of 0.29 x 10-3.

Superfast Lombard response in free-flying, echolocating bats

Acoustic cues are crucial to communication, navigation, and foraging in many animals, which hence face the problem of detecting and discriminating these cues in fluctuating noise levels from natural or anthropogenic sources. Such auditory dynamics are perhaps most extreme for echolocating bats that navigate and hunt prey on the wing in darkness by listening for weak echo returns from their powerful calls in complex, self-generated umwelts.1,2 Due to high absorption of ultrasound in air and fast flight speeds, bats operate with short prey detection ranges and dynamic sensory volumes,3 leading us to hypothesize that bats employ superfast vocal-motor adjustments to rapidly changing sensory scenes. To test this hypothesis, we investigated the onset and offset times and magnitude of the Lombard response in free-flying echolocating greater mouse-eared bats exposed to onsets of intense constant or duty-cycled masking noise during a landing task. We found that the bats invoked a bandwidth-dependent Lombard response of 0.1-0.2 dB per dB increase in noise, with very short delay and relapse times of 20ms in response to onsets and termination of duty-cycled noise. In concert with the absence call time-locking to noise-free periods, these results show that free-flying bats exhibit a superfast, but hard-wired, vocal-motor response to increased noise levels. We posit that this reflex is mediated by simple closed-loop audio-motor feedback circuits that operate independently of wingbeat and respiration cycles to allow for rapid adjustments to the highly dynamic auditory scenes encountered by these small predators.