[Level and health evaluation of rare earth elements in human blood and hair in the mining areas in Northwest Hubei].

OBJECTIVE: To investigate the content of rare earth elements(REs)in blood and hair of residents in a RE mining area in Northwest Hubei, and evaluate the impact of REs on the health status of local residents. METHODS: A total of 191 residents from the core area of RE mining areas and 186 residents from non RE mining areas, aged 20-69, were selected. The content of REs in the blood and hair of the survey subjects was measured using inductively coupled plasma mass spectrometry, and compared with existing literature values. At the same time, blood tests and questionnaire surveys will be conducted on the health status of residents to examine whether human RE enrichment can lead to endemic diseases. RESULTS: The average total content of REs in the blood of residents in the mining area was 60.22 ng/mL, which was 3.35 times that of the control area; The average total content of REs in hair was 1197.91 ng/g, which was 6.32 times higher than the control area. As age increasing, the abundance of REs in the blood and hair of both men and women in mining areas increased. The proportion of Yttrium and Scandium in the blood and hair were much higher than that in the soil. Compared to hair, Yttrium and Scandium were more easily enriched in the blood. There was no significant difference in the probability of fatty liver, hepatitis B, hypoglycemia, hypotension, hypertension and heart disease and the average life span between residents in RE mining areas and those in the control area. CONCLUSION: The high daily average dietary intake of REs in residents leads to a relatively large accumulation of REs in human blood and hair, but no significant and substantial human health damage has been found at present.

Machine learning insights into PM2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad.

This study seeks to investigate the impact of COVID-19 lockdown measures on air quality in the city of Mashhad employing two strategies. We initiated our research using basic statistical methods such as paired sample t-tests to compare hourly PM2.5 data in two scenarios: before and during quarantine, and pre- and post-lockdown. This initial analysis provided a broad understanding of potential changes in air quality. Notably, a low reduction of 2.40% in PM2.5 was recorded when compared to air quality prior to the lockdown period. This finding highlights the wide range of factors that impact the levels of particulate matter in urban settings, with the transportation sector often being widely recognized as one of the principal causes of this issue. Nevertheless, throughout the period after the quarantine, a remarkable decrease in air quality was observed characterized by distinct seasonal patterns, in contrast to previous years. This finding demonstrates a significant correlation between changes in human mobility patterns and their influence on the air quality of urban areas. It also emphasizes the need to use air pollution modeling as a fundamental tool to evaluate and understand these linkages to support long-term plans for reducing air pollution. To obtain a more quantitative understanding, we then employed cutting-edge machine learning methods, such as random forest and long short-term memory algorithms, to accurately determine the effect of the lockdown on PM2.5 levels. Our models' results demonstrated remarkable efficacy in assessing the pollutant concentration in Mashhad during lockdown measures. The test set yielded an R-squared value of 0.82 for the long short-term memory network model, whereas the random forest model showed a calculated cross-validation R-squared of 0.78. The required computational cost for training the LSTM and the RF models across all data was 25 min and 3 s, respectively. In summary, through the integration of statistical methods and machine learning, this research attempts to provide a comprehensive understanding of the impact of human interventions on air quality dynamics.

Spatiotemporal variations, health risk assessment, and sources of potentially toxic elements in potamic water of the Anday Stream Basin (Türkiye), Black Sea Region.

Monitoring and protecting freshwater habitats are paramount for a sustainable water management perspective. This study investigated potentially toxic elements (PTEs) in the potamic water of the Anday Stream Basin (Türkiye), Black Sea Region, for a hydrological year (from May 2020 to April 2021). Among PTEs, the highest average values were recorded for sodium (Na) at 41.3 mg/L and the lowest for mercury (Hg) at 0.009 µg/L and noted under quality guidelines. The stream was found to be at the level of "Low Heavy Metal Pollution" and "Low Contamination" based on the ecotoxicological risk indices. The highest calculated hazard quotient (HQ) value of 1.21E-02 for Cd was noted in the children via the dermal pathway and the lowest of 6.91E-06 for Fe in adults via the ingestion pathway. Results revealed a higher hazard index (HI) value of 1.50E-02 for Cd to children and the lowest of 1.98E-05 for Fe to adults. As a result of applying agricultural risk indices, the stream showed sodium adsorption ratio values less than 6 and was found to be "Excellent" for agriculture. However, the sodium percentage values were less than 20 and found "Permissible" and the magnesium hazard > 50 and noted as "Unsuitable" for agriculture. Statistical analysis revealed that natural factors mainly attributed to PTE contamination of the Anday Stream Basin.

Bark thickness and related parameters of tree species along an elevation transect leading to treeline in Central Himalaya.

Since treelines are generally fire-free, the trees growing there are expected to have thin bark, unless adaptation to other factors than fire results in the selection of a thick bark. Related to this is also higher proportional investment in inner bark in such an environment of infrequent fire. This study has considered stem bark thickness both in absolute and relative terms and also in the frame of the composition of outer and inner bark components of 20 tree species along an elevation transect (2100-3300 m) in high ranges of the Central Himalaya leading to treelines. The study species varied from 2.1 to 16.2 mm for total bark thickness and from 1.2 to 18.85% for relative bark thickness. The average absolute total bark thickness across the tree species decreased with elevation from forest to treeline, both when trees of all diameters (10.2 ± 0.84 mm for forest and 6.9 ± 1.79 mm for treeline) and those of the same stem diameter range (18-20 m) were compared (9.10 ± 1.30 mm for forest species and 6.38 ± 1.31 mm for treeline species). Nevertheless, the treeline bark thickness was similar to those of several forest communities considered to have comparatively thick bark. Like many other biological structures, bark carries out multiple functions; therefore, its thickness could be affected by more than one environmental factor. We suggest that the requirement of mechanical resistance to the snowfall, rainstorms, wind and adaptation to a high sunlight and UV radiations or storage of water, and non-structural carbohydrates could affect total, outer and inner bark thickness. Studies on these aspects in similar ecosystems may help understand the multi-functional attributes of the bark. For trees of comparable sizes (trees with 18-20 cm diameter at breast height) treeline species also had lower relative bark thickness (< 6%) than trees of forest below it (> 7%). The median proportion of inner bark of the total bark (70.5%) for our 20 species was more than that for savannas (~ 50%), exposed to frequent fire regime and similar to those of in cool sclerophyllous forests and temperate rain forests where fire return time is > 100 years. However, it was lower than the inner bark proportion reported for tropical rain forests. To conclude, in spite of a fire-free environment, the Himalayan treeline and adjoining forest species show mixed bark characters.

Research on smart city public health detection system and improvement technology based on intelligent multi-objective.

Introduction: With the increase of urban population density, urban sanitation becomes more severe; urban sanitation has important influence on public health. Therefore, in order to realize the detection of public health in smart cities, the research will use cutting-edge scientific and technological methods to improve urban environmental health, so as to promote the realization of public health achievements. This study introduces public health detection and optimizationtechnologies for smart cities. Methods: Firstly, a data detection system for urban public health environment was established using sensors and intelligent multi-objective technology to evaluate the water quality, air quality, and noise level of the city. Then, an intelligent garbage management system based on Tensor-flow was constructed to achieve efficient garbage collection and treatment. Finally, an intelligent traffic management system was developed to monitor and regulate urban traffic flow. Results: The results of the simulation experiment demonstrated that the life data detection system was operationally stable, with a high success rate of 98%. Furthermore, its accuracy in detecting residents' living environment data was above 95%, the maximum relative error was only 0.0465, making it a reliable and efficient tool. The waste recycling system achieved a minimum accuracy of 83.6%, the highest accuracy rate was 95.3%, making it capable of sorting and recycling urban waste effectively. Additionally, the smart traffic management system led to a 20% reduction in traffic congestion rates, 20 tonnes less tailpipe emissions and an improvement in public health and well-being. Discussion: In summary, the plan proposed in this study aims to create a more comfortable, safe, and healthy urban public health environment, while providing theoretical support for environmental health management in smart cities.

Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins.

Accurate and frequent nitrate estimates can provide valuable information on the nitrate transport dynamics. The study aimed to develop a data-driven modeling framework to estimate daily nitrate concentrations at low-frequency nitrate monitoring sites using the daily nitrate concentration and stream discharge information of a neighboring high-frequency nitrate monitoring site. A Long Short-Term Memory (LSTM) based deep learning (DL) modeling framework was developed to predict daily nitrate concentrations. The DL modeling framework performance was compared with two well-established statistical models, including LOADEST and WRTDS-Kalman, in three selected basins in Iowa, USA: Des Moines, Iowa, and Cedar River. The developed DL model performed well with NSE >0.70 and KGE >0.70 for 67% and 79% nitrate monitoring sites, respectively. DL and WRTDS-Kalman models performed better than the LOADEST in nitrate concentration and load estimation for all low-frequency sites. The average NSE performance of the DL model in daily nitrate estimation is 20% higher than that of the WRTDS-Kalman model at 18 out of 24 sites (75%). The WRTDS-Kalman model showed unrealistic fluctuations in the estimated daily nitrate time series when the model received limited observed nitrate data (less than 50) for simulation. The DL model indicated superior performance in winter months' nitrate prediction (60% of cases) compared to WRTDS-Kalman models (33% of cases). The DL model also better represented the exceedance days from the USEPA maximum contamination level (MCL). Both the DL and WRTDS-Kalman models demonstrated similar performance in annual stream nitrate load estimation, and estimated values are close to actual nitrate loads.

Comprehensive characterization of unscientifically disposed municipal solid waste (MSW) in Kashmir Region, India.

Unscientific dumping of municipal solid waste (MSW) is a common practice in Kashmir. To have an environmentally friendly and sustainable waste management system, MSW was collected from nine study locations of this region. They were air-dried, then oven-dried at 105 °C for 24 h, segregated, and characterized for various components. The overall average organic waste was > 55%, plastic waste about 17%, inert material about 10%, paper 9%, and cloth waste 7%. The calorific value of paper and plastic wastes exhibited was 4910 kcal/kg, while organic waste had a calorific value of 1980 kcal/kg. The proximate analysis showed that the moisture content ranged from 16 to 29%, volatile matter ranged from 49 to 72%, ash content ranged from 0.03 to 5%, and fixed carbon ranged from 5 to 20%. In S7, the volatile matter content recorded the lowest value at 49.15%, while in S5, the volatile matter content was notably higher at 71.84%, indicating easier ignition. Further, elemental analysis revealed that the major elements in MSW were carbon and oxygen, 53% and 37%, respectively, with small traces of heavy metals with an average of 0.02% cadmium (Cd) and 0.006% lead (Pb). Moreover, field emission scanning electron microscopy (FESEM) micrographs provided confirmation that the majority of components in the MSW exhibited either partial or complete degradation, resulting in a rough surface texture. In addition, the presence of silica and other silicate groups was also detected. Fourier transform infrared spectroscopy (FT-IR) analysis revealed that the main functional groups were alcohol. In the X-ray diffraction (XRD) analysis, all the major mineral phases were detected between 20 and 30° 2θ, except for the peaks at 50-60° 2θ in S3 and S9 where catalysts such as zeolite Y and zeolite X were detected. Overall, the MSW had low moisture content but higher calorific value, making it a viable feedstock.

Physicochemical properties and heavy metal concentration of soils along Enyigba mining site in Ikwo, Ebonyi State Nigeria.

This study assessed the physicochemical properties of soils and their levels of heavy metal contents in soils along the Enyigba mining site in Ikwo, Ebonyi State, Nigeria. A total of 96 samples of soil were taken at depths of 0 to 20 cm using a soil auger and core sampler at a horizontal spacing of 100 m between each location and examined using standard laboratory techniques. The control soil samples were taken from the Alex Ekwueme Federal University Experimental and Research Farm in Ebonyi State, at a distance of 50 m from each spot at a depth of 0 to 20 cm. The results obtained from this study showed significant variations in the physicochemical properties and heavy metal levels of the soil from the Enyigba mining site, indicating that the mining activities have contaminated the soil. The result also indicated that mining operations may be responsible for the increase in sand and the decrease in silt and clay particles. The mining site's pH was typically low, indicating that the soil is naturally acidic. The contamination indices showed that lead recorded very high contamination factor of 27.068, while iron, nickel and zinc were low. The observed high concentration factor of lead had an impact on the soil's bulk density, saturated hydraulic conductivity, total porosity, calcium, potassium ion, magnesium ion, total nitrogen, organic carbon, cation exchange capacity, phosphorus and base saturation contents. It is recommended that the government's Ministry of Environment, at all levels, take a proactive stance in managing the excessive and subpar mining operations in the study area.

Integrated management of groundwater quantity, physicochemical properties, and microbial quality in West Nile delta using a new MATLAB code and geographic information system mapping.

Groundwater is an excellent alternative to freshwater for drinking, irrigation, and developing arid regions. Agricultural, commercial, industrial, residential, and municipal activities may affect groundwater quantity and quality. Therefore, we aimed to use advanced methods/techniques to monitor the piezometric levels and collect groundwater samples to test their physicochemical and biological characteristics. Our results using software programs showed two main types of groundwater: the most prevalent was the Na-Cl type, which accounts for 94% of the groundwater samples, whereas the Mg-Cl type was found in 6% of samples only. In general, the hydraulic gradient values, ranging from medium to low, could be attributed to the slow movement of groundwater. Salinity distribution in groundwater maps varied between 238 and 1350 mg L-1. Although lower salinity values were observed in northwestern wells, higher values were recorded in southern ones. The collected seventeen water samples exhibited brackish characteristics and were subjected to microbial growth monitoring. Sample WD12 had the lowest total bacterial count (TBC) of 4.8 ± 0.9 colony forming unit (CFU mg L-1), while WD14 had the highest TBC (7.5 ± 0.5 CFU mg L-1). None of the tested water samples, however, contained pathogenic microorganisms. In conclusion, the current simulation models for groundwater drawdown of the Quaternary aquifer system predict a considerable drawdown of water levels over the next 10, 20, and 30 years with the continuous development of the region.

Adsorption behavior and the potential risk of As(V) in soils: exploring the effects of representative surfactants.

Arsenic contamination in soils poses a critical global challenge, yet the influence of surfactants on arsenic adsorption behavior is often underestimated. This study aims to investigate the effects of three representative surfactants, namely cetyltrimethylammonium bromide (CTAB), sodium dodecyl sulfate (SDS), and polyethylene glycol anhydrous sugar alcohol monooleate (Tween 80), on arsenic adsorption behavior in soils. The adsorption isotherm shifts from a single Temkin model without surfactants to both the Langmuir and Temkin models in the presence of surfactants, indicating the simultaneous occurrence of monolayer and multilayer adsorption for arsenic in soils. Moreover, the surfactants can inhibit the adsorption and hasten the attainment of adsorption equilibrium. SDS displayed the most inhibitory effect on arsenic adsorption, followed by Tween 80 and CTAB, due to the competitive adsorption, electrostatic interaction, and hydrophobic interaction. Variations in zeta potential with different surfactants further elucidate this inhibitory phenomenon. Through orthogonal experiment analyses, pH emerges as a primary factor influencing arsenic adsorption in soils, with surfactant concentration and type identified as secondary factors. Temperature notably affects CTAB, with the adsorption inhibition rate plummeting to a mere 0.88% at 50 °C. Sequential extraction analysis revealed that surfactants enhanced the bioavailability of arsenic. The FTIR, XRD, SEM, and CA analyses further support the mechanism underlying the effect of surfactants on arsenic adsorption in soil. These analyses indicate that surfactants modify the composition and abundance of functional groups, hinder the formation of arsenic-containing substances, and improve soil compactness, smoothness, and hydrophilicity. This study provides valuable insights into the effect of surfactants in arsenic-contaminated soils, which is often ignored in previous work.

COVID-19 fatality and DALYs, and associated metabolic disorders and ambient air pollutants in pre-Omicron era of the pandemic: an international comparative study.

BACKGROUND: Air pollution and a number of metabolic disorders have been reported to increase the risk of severe COVID-19 outcomes. This study explored the association between severe COVID-19 outcomes, metabolic disorders and environmental air pollutants, at regional level, across 38 countries. METHODS: We conducted an ecological study using COVID-19 data related to countries of the Organization for Economic Cooperation and Development (OECD), with an estimated population of 1.4 billion. They were divided into 3 regions: 1. Europe & Middle east; 2. Americas (north, central & south America); 3. East-Asia & West Pacific. The outcome variables were: COVID-19 case-fatality rate (CFR) and disability-adjusted life years (DALYs) at regional level. Freely accessible datasets related to regional DALYs, demographics and other environmental pollutants were obtained from OECD, WHO and the World in Data websites. Generalized linear model (GLM) was performed to determine the regional determinants of COVID-19 CFR and DALYs using the aggregate epidemiologic data (Dec. 2019-Dec. 2021). RESULTS: Overall cumulative deaths were 65,000 per million, for mean CFR and DALYs of 1.31 (1.2)% and 17.35 (2.3) years, respectively. Globally, GLM analysis with adjustment for elderly population rate, showed that COVID-19 CFR was positively associated with atmospheric PM2.5 level (beta = 0.64(0.0), 95%CI: 0.06-1.35; p < 0.05), diabetes prevalence (beta = 0.26(0.1), 95%CI: 0.12-0.41; p < 0.001). For COVID-19 DALYs, positive associations were observed with atmospheric NOx level (beta = 0.06(0.0), 95%CI: 0.02-0.82; p < 0.05) and diabetes prevalence (beta = 0.32(0.2), 95%CI: 0.04-0.69; p < 0.05). At regional level, adjusted GLM analysis showed that COVID-19 CFR was associated with atmospheric PM2.5 level in the Americas and East-Asia & Western Pacific region; it was associated with diabetes prevalence for countries of Europe & Middle east and East-Asia & Western Pacific region. Furthermore, COVID-19 DALYs were positively associated with atmospheric PM2.5 and diabetes prevalence for countries of the Americas only. CONCLUSION: These findings confirm that diabetes and air pollution increase the risk of disability and fatality due to COVID-19, with disparities in terms of their impact. They suggest that efficient preventive and management programs for diabetes and air pollution countermeasures would have curtailed severe COVID-19 outcome rates.

Outdoor fine particulate matter exposure and telomere length in humans: A systematic review and meta-analysis.

Although the association between changes in human telomere length (TL) and ambient fine particulate matter (PM2.5) has been documented, there remains disagreement among the related literature. Our study conducted a systematic review and meta-analysis of epidemiological studies to investigate the health effects of outdoor PM2.5 exposure on human TL after a thorough database search. To quantify the overall effect estimates of TL changes associated with every 10 µg/m3 increase in PM2.5 exposure, we focused on two main topics, which were outdoor long-term exposure and prenatal exposure of PM2.5. Additionally, we included a summary of short-term PM2.5 exposure and its impact on TL due to limited data availability. Our qualitative analysis included 20 studies with 483,600 participants. The meta-analysis showed a statistically significant association between outdoor PM2.5 exposure and shorter human TL, with pooled impact estimates (ß) of -0.12 (95% CI: -0.20, -0.03, I2= 95.4%) for general long-term exposure and -0.07 (95% CI: -0.15, 0.00, I2= 74.3%) for prenatal exposure. In conclusion, our findings suggest that outdoor PM2.5 exposure may contribute to TL shortening, and noteworthy associations were observed in specific subgroups, suggesting the impact of various research variables. Larger, high-quality studies using standardized methodologies are necessary to strengthen these conclusions further.

Predicting the impacts of urban development on urban thermal environment using machine learning algorithms in Nanjing, China.

The urban thermal environment undergoes significant influences from changes in land use/land cover (LULC). This article uses CA-ANN and ANN algorithms to forecast LULC and changes in the urban thermal environment in Nanjing for the years 2030 and 2040. It investigates the interplay between LULC changes, land surface temperature (LST), and the urban thermal field variance index (UTFVI). The findings reveal that urban land exhibited a significant expansion trend from 2000 to 2019, reaching 1083.43 km2 in 2019. The forecast indicates that urban land may increase by 8.79% and 10.92% by 2030 and 2040, respectively. Conversely, vegetation and bare land may decrease. The LST is likely to continue to rise, accompanied by a significant expansion of the high temperature range and a contraction of the low temperature range. By 2030 and 2040, the area with LST<20 °C is likely to decrease by 2.17% and 3.19%, while the area with LST>30 °C is likely to expand by 5.68% and 8.08%, respectively. The UTFVI area of urban land may decrease at none and middle levels but may notably expand at stronger and strongest levels. The areas with UTFVI at none, weak, and middle levels show a declining trend, while the increase in UTFVI at the strong level may exceed 46.29% and the strongest level of UTFVI may continue to expand. This study offers new insights into urban sustainable development and thermal environment governance.

Predictive modeling of Salmonella spp. growth behavior in cooked and raw chicken samples: Real-time PCR quantification approach and model assessment in different handling scenarios.

The increasing prevalence of Salmonella contamination in poultry meat emphasizes the importance of suitable predictive microbiological models for estimating Salmonella growth behavior. This study was conducted to evaluate the potential of chicken juice as a model system to predict the behavior of Salmonella spp. in cooked and raw chicken products and to assess its ability to predict cross-contamination scenarios. A cocktail of four Salmonella serovars was inoculated into chicken juice, sliced chicken, ground chicken, and chicken patties, with subsequent incubation at 10, 15, 20, and 25°C for 39 h. The number of Salmonella spp. in each sample was determined using real-time polymerase chain reaction. Growth curves were fitted into the primary Baranyi and Roberts model to obtain growth parameters. Interactions between temperature and growth parameters were described using the secondary Ratkowsky's square root model. The predictive results generated by the chicken juice model were compared with those obtained from other chicken meat models. Furthermore, the parameters of the chicken juice model were used to predict Salmonella spp. numbers in six worst-case cross-contamination scenarios. Performance of the chicken juice model was evaluated using the acceptable prediction zone from -1.0 (fail-safe) to 0.5 (fail-dangerous) log. Chicken juice model accurately predicted all observed data points within the acceptable range, with the distribution of residuals being wider near the fail-safe zone (75%) than near the fail-dangerous zone (25%). This study offers valuable insights into a novel approach for modeling Salmonella growth in chicken meat products, with implications for food safety through the development of strategic interventions. PRACTICAL APPLICATION: The findings of this study have important implications in the food industry, as chicken juice could be a useful tool for predicting Salmonella behavior in different chicken products and thus reducing the risk of foodborne illnesses through the development of strategic interventions. However, it is important to recognize that some modifications to the chicken juice model will be necessary to accurately mimic all real-life conditions, as multiple factors particularly those related to food processing can vary between different products.

Assessment of the stabilization effect of ferrous sulfate for arsenic-contaminated soils based on chemical extraction methods and in vitro methods: Methodological differences and linkages.

Stabilization of arsenic-contaminated soils with ferrous sulfate has been reported in many studies, but there are few stabilization effects assessments simultaneously combined chemical extraction methods and in vitro methods, and further explored the corresponding alternative relationships. In this study, ferrous sulfate was added at FeAs molar ratio of 0, 5, 10 and 20 to stabilize As in 10 As spiked soils. Stabilization effects were assessed by 6 chemical extraction methods (toxicity characteristic leaching procedures (TCLP), HCl, diethylenetriamine pentaacetic acid (DTPA), CaCl2, CH3COONH4, (NH4)2SO4), and 4 in vitro methods (physiologically based extraction test (PBET), in vitro gastrointestinal method (IVG), Solubility Bioaccessibility Research Consortium (SBRC) method, and the Unified Bioaccessibility Research Group of Europe method (UBM)). The results showed that the HCl method provides the most conservative assessment results in non-calcareous soils, and in alkaline calcareous soils, (NH4)2SO4 method provides a more conservative assessment. In vitro methods provided significantly higher As concentrations than chemical extraction methods. The components of the simulated digestion solution as well as the parameters may have contributed to this result. The small intestinal phase of PBET and SBRC method produced the highest and lowest ranges of As concentrations, and in the range of 127-462 mg/kg and 68-222 mg/kg when the FeAs molar ratio was 5. So the small intestinal phase of PBET method may provide the most conservative assessment results, while the same phase of SBRC may underestimate the human health risks of As in stabilized soil by 51 %(at a FeAs molar ratio of 5). Spearman correlation analysis indicated that the small intestinal phase of PBET method correlated best with HCl method (correlation coefficient: 0.71). This study provides ideas for the assessment of stabilization efforts to ensure that stabilization meets ecological needs while also being less harmful to humans.

Does national forest city improve residents' health? Evidence from China.

Objective: National health is essential for economic and social development. The aim of this article is to examine the relationship, heterogeneity effects and influential mechanisms between National Forest Cities and the residents' health. Methods: The article matches the China Family Panel Studies data in 2018 (CFPS2018) with the 2016-2018 National Forest Cities Construction List, resulting in a final sample of 20,041. Oprobit, Ologit, Instrumental Variable technique (2SLS) and interaction term analysis were used as the main research methods in this article. Results: The findings indicate that: (1) The construction of National Forest Cities significantly improves the residents' health in terms of both physical and mental health, and this conclusion is still valid after a series of robustness tests. (2) On the one hand, National Forest Cities promote residents' health by reducing air pollutants such as SO2 and soot to reduce residents' health risk exposure; On the other hand, it promotes residents' health by positively guiding them to engage in healthy behaviors. (3) National Forest Cities have a greater effect on the health of urban residents, older adult and lower-income group, suggesting that National Forest Cities are a public benefit. Conclusions: The construction of National Forest Cities is a public welfare that promotes residents' health, and it is an important revelation for accelerating the realization of the Healthy China Strategy. The article provides new empirical evidence for understanding the welfare effects of forest cities and offers new practical paths for improving residents' health.

[Characteristics and Risk Assessment of Antibiotic Contamination in Oujiang River Basin in Southern Zhejiang Province].

Antibiotic pollution in the environment has a negative impact on ecosystem security. Taking the Oujiang River Basin as an example,high-performance liquid chromatography mass spectrometry(LC-MS)was used to detect the concentration of six classes of 35 antibiotics in the surface water of the southern Zhejiang River Basin. The concentration level and spatial distribution of antibiotics were analyzed,the risk of antibiotics to ecology and human health were assessed using relevant models,and the sources of antibiotics were discussed. The results showed that in 20 sampling sites,a total of four classes of 12 antibiotics were detected,including sulfonamides,quinolones,tetracyclines,and lincosamides. The total concentration was ND-1 018 ng·L-1. The highest detection rate was that of Lincomycin(90.48%),followed by that of sulfapyridine(38.10%). The three antibiotics with the highest average concentrations were ofloxacin(12.49 ng·L-1),Lincomycin(11.08 ng·L-1),and difloxacin(7.38 ng·L-1). Antibiotics in the basin showed mainly spotty pollution,which had large spatial differentiation. The average concentration of antibiotics in the upstream(54.39 ng·L-1)was higher than that mid-downstream(46.64 ng·L-1). The degree of antibiotic pollution from upstream to downstream showed a characteristic of being "sparse in the upstream and dense in the downstream. " This indicated that the concentration of antibiotics in the upstream was significantly different,whereas the pollution degree of antibiotics in the downstream was uniform. The upstream was mainly polluted by health,livestock,and poultry breeding wastewater emissions,and downstream pollution was mainly caused by densely populated activities and the rapid development of economy,trade,and industry. The ecological risk assessment results showed that the upstream site H6 had the highest risk quotient,ofloxacin and enrofloxacin had high risk levels, and lincomycin had a moderate risk level. Health risk assessment results showed that the Oujiang River surface water antibiotics posed no risk to human health.

[Impacts of Anthropogenic Emission Reduction on Urban Atmospheric Oxidizing Capacity During the COVID-19 Lockdown].

In recent years， regional compound air pollution events caused by fine particles （PM2.5） and ozone （O3） have occurred frequently in economically developed areas of China， in which atmospheric oxidizing capacity （AOC） has played an important role. In this study， the WRF-CMAQ model was used to study the impacts of anthropogenic emission reduction on AOC during the COVID-19 lockdown period. Three representative cities in eastern China （Shijiazhuang， Nanjing， and Guangzhou） were selected for an in-depth analysis to quantify the contribution of meteorology and emissions to the changes in AOC and oxidants and to discuss the impact of AOC changes on the formation of secondary pollutants. The results showed that， compared with that in the same period in 2019， the urban average AOC in Shijiazhuang， Nanjing， and Guangzhou in 2020 increased by 60%， 48.7%， and 12.6%， respectively. The concentrations of O3， hydroxyl radical （·OH）， and nitrogen trioxide （NO3·ï¼‰ increased by 1.6%-26.4%， 14.8%-73.3%， and 37.9%-180%， respectively. The AOC in the three cities increased by 0.06×10-4， 0.12×10-4， and 0.33×10-4 min-1， respectively， due to emission reduction. The meteorological change increased AOC in Shijiazhuang and Nanjing by 20% and 17.9%， respectively， but decreased AOC in Guangzhou by -9.3%. Enhanced AOC led to an increase in the nitrogen oxidation ratio （NOR） and VOCs oxidation ratio （VOR） and promoted the transformation of primary pollutants to secondary pollutants. This offset the effects of primary emission reduction and resulted in a nonlinear decline in secondary pollutants compared to emissions during the COVID-19 lockdown.

The impact of COVID-19 pandemic on air particulate matter exposure and heart attacks: a 5-year retrospective cohort study in Taiwan (2017-2021).

Background: Heart attacks including acute ST-segment elevation myocardial infarction (STEMI) and acute decompensated heart failure (ADHF) caused from the particulate matter (PM) and air pollutant exposures are positively associated with regional air pollution severity and individual exposure. The exceptional coronavirus disease epidemic of 2019 (COVID-19) may enhance the air conditions in areas under COVID-19 pandemic. We sought to study the impact of COVID-19 pandemic on air particulate matter (PM) exposure and heart attacks in Taiwan. Methods: This retrospective cohort study was conducted in one teaching hospital in Taichung, Taiwan. We examined emergency patients diagnosed with acute STEMI and ADHF from January 1, 2017, to March 31, 2020, (i.e., before the COVID-19 pandemic) and from April 1, 2020, to December 31, 2021, (after the COVID-19 pandemic). The effects of particulate matter with a diameter of less than 2.5 micrometers (PM2.5) and PM10 as well as temperature and humidity on environmental air pollutants were recorded. The analysis was performed with a unidirectional case-crossover research design and a conditional logistic regression model. Results: Both PM2.5 and PM10 levels had a positive association with the risk of acute STEMI before the COVID-19 pandemic (PM2.5 adjusted odds ratio (OR): 1.016, 95% confidence interval (CI): 1.003-1.032 and PM10 adjusted OR: 1.009, 95% CI: 1.001-1.018) and ADHF (PM2.5 adjusted OR: 1.046, 95% CI: 1.034-1.067 and PM10 adjusted OR: 1.023, 95% CI: 1.027-1.047). Moreover, the results demonstrated that PM2.5 and PM10 were not associated with the risk of acute STEMI or ADHF after the COVID-19 pandemic. Reduction in PM2.5 and PM10 levels after the COVID-19 pandemic were noted. Hospital admissions for acute STEMI (7.4 and 5.8/per month) and ADHF (9.7 and 8.2/per month) also decreased (21.6 and 15.5%) after the COVID-19 pandemic. Conclusion: In Taiwan, paradoxical reductions in PM2.5 and PM10 levels during the COVID-19 pandemic may decrease the number of hospital admissions for acute STEMI and ADHF. As the COVID-19 pandemic eases, the condition of air pollution may gradually become worse again. The governments should formulate better policies to improve the health of the public and the quality of the air.

Fertilisation of agricultural soils with municipal biosolids: Glyphosate and aminomethylphosphonic acid inputs to Québec field crop soils.

Municipal biosolids (MBS) are suggested to be abundant, sustainable, inexpensive fertilisers, rich in phosphorus and nitrogen. However, MBS can also contain glyphosate and phosphonates that can degrade to AMPA. Glyphosate-based herbicides (GBH) are used in field crops all over the world. Most glyphosate generally degrades within a few weeks, mainly as aminomethylphosphonic acid (AMPA). AMPA is more persistent than glyphosate, and can accumulate from one crop year to the next. AMPA is phytotoxic even to glyphosate-resistant crops. The aims of this study were to assess whether MBS applications constitute: 1) an additional source of glyphosate and AMPA to agricultural soils with respect to GBH, 2) a significant source of trace metals, and 3) a partial replacement of mineral fertilisation while maintaining similar yields. To this end, four experimental agricultural sites were selected in Québec (Canada). Soil samples (0-20 cm) were collected to estimate the as yet unmeasured contribution of MBS application to glyphosate and AMPA inputs in agricultural soils. MBS applied in 2021 and 2022 had mean concentrations of 0.69 ± 0.53 µg glyphosate/dry g and 6.26 ± 1.93 µg AMPA/dry g. Despite the presence of glyphosate and AMPA in MBS, monitoring of these two compounds in corn and soybean crops over two years showed no significant difference between plots treated with and without MBS applications. For the same site, yields measured at harvest were similar between treatments. MBS application could thus represent a partial alternative to mineral fertilisers for field crops, while limiting the economic and environmental costs associated with their incineration and landfilling. It is also an economic advantage for agricultural producers given the possibility of using fewer mineral fertilisers and therefore reducing the environmental impact of their use.