Search Results (11,276)

Environmental pollution continues to increase with industrial development and has become a threat to human health. Atmospheric particulate matter (PM) was designated as a Group 1 carcinogen by the International Agency for Research on Cancer in 2013 and is an emerging global environmental risk factor that is a major cause of death related to cardiovascular and respiratory diseases. PM is a complex composed of highly reactive organic matter, chemicals, and metal components, which mainly cause excessive production of reactive oxygen species (ROS) that can lead to DNA and cell damage, endoplasmic reticulum stress, inflammatory responses, atherosclerosis, and airway remodeling, contributing to an increased susceptibility to and the exacerbation of various diseases and infections. PM has various effects on human health depending on the particle size, physical and chemical characteristics, source, and exposure period. PM smaller than 5 μm can penetrate and accumulate in the alveoli and circulatory system, causing harmful effects on the respiratory system, cardiovascular system, skin, and brain. In this review, we describe the relationship and mechanism of ROS-mediated cell damage, oxidative stress, and inflammatory responses caused by PM and the health effects on major organs, as well as comprehensively discuss the harmfulness of PM. Full article

To increase productivity, more sophisticated cluster tools are developed. To achieve this, one of the ways is to increase the number of spaces in a process module (PM) and the number of fingers on a robot arm as well, leading to a cluster tool with multi-space PMs and a multi-finger-arm robot. This paper discusses the scheduling problem of cluster tools with four-space PMs and a four-finger-arm robot, a typical tool with multi-space PMs and a multi-finger-arm robot adopted in modern fabs. With two arms in such a tool, one is used as a clean one, while the other is used as a dirty one. In this way, wafer quality can be improved. However, scheduling such cluster tools to ensure the residency time constraints is very challenging, and there is no research report on this issue. This article conducts an in-depth analysis of the steady-state scheduling for this type of cluster tools to explore the effect of different scheduling strategies. Based on the properties, four robot task sequences are presented as scheduling strategies. With them, four linear programming models are developed to optimize the cycle time of the system and find feasible schedules. The performance of these strategies is dependent on the activity parameters. Experiments are carried out to test the effect of different parameters on the performance of different strategies. It shows that, given a group of parameters, one can apply all the strategies and choose the best result obtained by one of the strategies. Full article

Aiming at the problems of small stems and irregular contours of bitter gourd, which lead to difficult and inaccurate location of picking points in the picking process of mechanical arms, this paper proposes an improved YOLOv5-seg instance segmentation algorithm with a coordinate attention (CA) mechanism module, and combines it with a refinement algorithm to identify and locate the picking points of bitter gourd. Firstly, the improved algorithm model was used to identify and segment bitter gourd and melon stems. Secondly, the melon stem mask was extracted, and the thinning algorithm was used to refine the skeleton of the extracted melon stem mask image. Finally, a skeleton refinement graph of bitter gourd stem was traversed, and the midpoint of the largest connected region was selected as the picking point of bitter gourd. The experimental results show that the prediction precision (P), precision (R) and mean average precision (mAP) of the improved YOLOv5-seg model in object recognition were 98.04%, 97.79% and 98.15%, respectively. Compared with YOLOv5-seg, the P, R and mA values were increased by 2.91%, 4.30% and 1.39%, respectively. In terms of object segmentation, mask precision (P(M)) was 99.91%, mask recall (R(M)) 99.89%, and mask mean average precision (mAP(M)) 99.29%. Compared with YOLOv5-seg, the P(M), R(M), and mAP(M) values were increased by 6.22%, 7.81%, and 5.12%, respectively. After testing, the positioning error of the three-dimensional coordinate recognition of bitter gourd picking points was X-axis = 7.025 mm, Y-axis =5.6135 mm, and Z-axis = 11.535 mm, and the maximum allowable error of the cutting mechanism at the end of the picking manipulator was X-axis = 30 mm, Y-axis = 24.3 mm, and Z-axis = 50 mm. Therefore, this results of study meet the positioning accuracy requirements of the cutting mechanism at the end of the manipulator. The experimental data show that the research method in this paper has certain reference significance for the accurate identification and location of bitter gourd picking points. Full article

The FAO56 Penman–Monteith model (FAO56-PM) is widely used for estimating reference crop evapotranspiration (ET₀). However, key variables such as shortwave radiation (R_s) and net longwave radiation (R_ln) are often unavailable at most weather stations. While previous studies have focused on calibrating R_s, the influence of large R_ln, particularly in high-altitude regions with thin air, remains unexplored. This study investigates this issue by using observed data from Bange in central Tibet to identify the optimal methods for estimating R_s and R_ln to accurately calculate ET₀. The findings reveal that the average daily R_ln was 8.172 MJ m⁻² d⁻¹ at Bange, much larger than that at the same latitude. The original FAO56-PM model may produce seemingly accurate ET₀ estimates due to compensating errors: underestimated R_ln offsetting underestimated net shortwave radiation (R_sn). Merely calibrating R_s does not improve ET₀ accuracy but may exacerbate errors. The Liu-S was the empirical model for R_s estimation calibrated by parameterization over the Tibetan Plateau and the Allen-LC was the empirical model for R_ln estimation calibrated by local measurements in central Tibet. The combination of the Liu-S and Allen-LC methods showed much-improved performance in ET₀ estimation, yielding a high Nash–Sutcliffe Efficiency (NSE) of 0.889 and a low relative error of −5.7%. This strategy is indicated as optimal for ET₀ estimation in central Tibet. Trend analysis based on this optimal strategy indicates significant increases in ET₀ in central Tibet from 2000 to 2020, with projections suggesting a continued rise through 2100 under climate change scenarios, though with increasing uncertainty over time. However, the rapidly increasing trends in precipitation will lead to decreasing trends in agricultural water use for highland parley production in central Tibet under climate change scenarios. The findings in this study provide critical information for irrigation planning to achieve sustainable agricultural production over the Tibetan Plateau. Full article

Urbanization produces spatially variable landscapes where climatic, environmental, and social systems interact in complex ways that affect public health. Environmental exposure along with the associated health risks are unevenly distributed and communities of color are often disproportionately affected by poor health outcomes. Acute pediatric asthma is the most common chronic condition of childhood in developed nations and is especially prevalent in minority and low-income children. In this study, we analyze the spatial variability of neighborhood-level acute pediatric asthma emergency department (ED) visits across the Kansas City Metro Area. Using Bayesian negative binomial regression, we describe the relationships and interactions between race, low income, fractional vegetation, and PM_2.5. We find significant disparities in acute pediatric asthma incidence in census tracts with different levels of poverty and percentages of non-White populations, even after accounting for neighborhood economic position. We also find that higher PM_2.5 concentrations are associated with increased asthma ED visits and that a high percentage of vegetative cover reduces this effect in high-pollution neighborhoods. The magnitude of this protective effect is stronger in neighborhoods with a high proportion of non-White residents. These results suggest that investing in greenspace infrastructure may reduce the deleterious effects of PM_2.5 and provide health benefits, especially in neighborhoods of color. Full article

Shrinking shorelines and the exposed playa of saline lakes can pose public health and air quality risks for local communities. This study combines a community science method with models to forecast future shorelines and PM10 air quality impacts from the exposed playa of the Salton Sea, near the community of North Shore, CA, USA. The community science process assesses the rate of shoreline change from aerial images collected through a balloon mapping method. These images, captured from 2019 to 2021, are combined with additional satellite images of the shoreline dating back to 2002, and analyzed with the DSAS (Digital Shoreline Analysis System) in ArcGIS desktop. The observed rate of change was greatly increased during the period from 2017 to 2020. The average rate of change rose from 12.53 m/year between 2002 and 2017 to an average of 38.44 m/year of shoreline change from 2017 to 2020. The shoreline is projected to retreat 150 m from its current position by 2030 and an additional 172 m by 2041. To assess potential air quality impacts, we use WRF-Chem, a regional chemical transport model, to predict increases in emissive dust from the newly exposed playa land surface. The model output indicates that the forecasted 20-year increase in exposed playa will also lead to a rise in the amount of suspended dust, which can then be transported into the surrounding communities. The combination of these model projections suggests that, without mitigation, the expanding exposed playa around the Salton Sea is expected to worsen pollutant exposure in local communities. Full article

Chiggers (chigger mites) are a group of tiny arthropods, and they are the exclusive vector of Orientia tsutsugamushi (Ot), the causative agent of scrub typhus (tsutsugamushi disease). Dehong Prefecture in Yunnan Province of southwest China is located on the China–Myanmar border and is an important focus of scrub typhus. Based on the field surveys in Dehong between 2008 and 2022, the present paper reports the infestation and ecological distribution of chiggers on the body surface of rodents and other sympatric small mammals (shrews, tree shrews, etc.) in the region for the first time. The constituent ratio (C_r), prevalence (P_M), mean abundance (MA), and mean intensity (MI) were routinely calculated to reflect the infestation of small-mammal hosts with chiggers. Additionally, the species richness (S), Shannon–Wiener diversity index (H), Simpson dominance index (D), and Pielou’s evenness index (E) were calculated to illustrate the chigger community structure. Preston’s log-normal model was used to fit the theoretical curve of species abundance distribution, and the Chao 1 formula was used to roughly estimate the expected total species. The “corrplot” package in R software (Version 4.3.1) was used to analyze interspecific relationships, and the online drawing software was used to create a chord diagram to visualize the host–chigger associations. From 1760 small-mammal hosts, a total of 9309 chiggers were identified as belonging to 1 family, 16 genera, and 117 species, with high species diversity. The dominant chigger species were Leptotrombidium deliense, Walchia ewingi, and Gahrliepia longipedalis, with a total C_r = 47.65% (4436/9309), among which L. deliense is the most important vector of Ot in China. The overall infestation indexes (P_M, MA, and MI) and community parameters (S, H, and E) of chiggers in the mountainous areas and outdoors were higher than those in the flatland areas and indoors, with an obvious environmental heterogeneity. Leptotrombidium deliense was the dominant species in the flatland and indoors, while G. longipedalis was the prevalent species in the mountainous and outdoor areas. The species abundance distribution of the chigger community conformed to log-normal distribution with the theoretical curve equation: ${S\left(R\right)}^{\prime }=28e^{{-[0.23(R-0)]}^2}$, indicating the existence of many rare species and only a few dominant species in the community. The expected total number of chigger species was roughly estimated to be 147 species, 30 more than the 117 species actually collected, suggesting that some uncommon species may have been missed in the sampling survey. The host–parasite association analysis revealed that one host species can harbor different chigger species, and one chigger species can parasitize different host species with low host specificity. A positive or negative correlation existed among different chigger species, indicating a cooperative or competitive interspecific relationship. The species diversity of chiggers is high in Dehong on the China–Myanmar border, and a large host sample is recommended to find more uncommon species. There is an obvious environmental heterogeneity of the chigger community, with different species diversity and dominant species in different environments. The low host specificity of chiggers and the occurrence of a large number of L. deliense in Dehong, especially in flatland areas and indoors, would increase the risk of persistent transmission of scrub typhus in the region. Full article

The lithium-ion battery (LIB) thermal runaway (TR) emits a wide size range of particles with diverse chemical compositions. When inhaled, these particles can cause serious adverse health effects. This study measured the size distributions of particles with diameters less than 10 µm released throughout the TR-driven combustion of cylindrical lithium iron phosphate (LFP) and pouch-style lithium cobalt oxide (LCO) LIB cells. The chemical composition of fine particles (PM_2.5) and some acidic gases were also characterized from filter samples. The emission factors of particle number and mass as well as chemical components were calculated. Particle number concentrations were dominated by those smaller than 500 nm with geometric number mean diameters below 130 nm. Mass concentrations were also dominated by smaller particles, with PM₁ particles making up 81–95% of the measured PM₁₀ mass. A significant amount of organic and elemental carbon, phosphate, and fluoride was released as PM_2.5 constituents. The emission factor of gaseous hydrogen fluoride was 10–81 mg/Wh, posing the most immediate danger to human health. The tested LFP cells had higher emission factors of particles and HF than the LCO cells. Full article

In recent years, the Swept-Source Phase Microscope (SS-PM) has gained more attention due to its greater robustness to sample motion and lower signal decay with depth. However, the mechanical wavelength tuning of the swept source creates small variations in the wavenumber sampling of spectra that introduce serious phase noise. We present a software post-processing method to eliminate phase noise in SS-PM. This method does not require high-quality swept light sources or high-precision synchronization devices and achieves ~72 pm displacement sensitivity using a conventional SS-PM system. We compare the performance of this method with traditional software-based methods by measuring phase fluctuations. The phase fluctuations in the traditional software-based method are five times those of the proposed method, which means the proposed method has better sensitivity. Using this method, we reconstructed phase images of air wedges and resolution plates to demonstrate the SS-PM’s potential for high-sensitivity surface profiling measurement. Finally, we discuss the advantages of SS-PM over traditional Spectral-Domain PM techniques. Full article

As urbanization and industrial activities accelerate globally, air quality has become a pressing concern, particularly due to the harmful effects of particulate matter (PM), notably PM_2.5 and PM₁₀. This review paper presents a comprehensive systematic assessment of machine learning (ML) techniques for estimating PM concentrations, drawing on studies published from 2018 to 2024. Traditional statistical methods often fail to account for the complex dynamics of air pollution, leading to inaccurate predictions, especially during peak pollution events. In contrast, ML approaches have emerged as powerful tools that leverage large datasets to capture nonlinear, intricate relationships among various environmental, meteorological, and anthropogenic factors. This review synthesizes findings from 32 studies, demonstrating that ML techniques, particularly ensemble learning models, significantly enhance estimation accuracy. However, challenges remain, including data quality, the need for diverse and balanced datasets, issues related to feature selection, and spatial discontinuity. This paper identifies critical research gaps and proposes future directions to improve model robustness and applicability. By advancing the understanding of ML applications in air quality monitoring, this review seeks to contribute to developing effective strategies for mitigating air pollution and protecting public health. Full article

Indoor air pollution, particularly in rural communities, is a significant health determinant, primarily due to the prevalence of traditional cooking practices. The WHO estimates 4.3 million annual deaths related to household air pollution. This study quantifies indoor pollutants and assesses health impacts and perceptions regarding traditional cooking. Using Extech air quality monitoring equipment, the study measured particulate matter (PM), carbon monoxide (CO), and carbon dioxide (CO₂) in 48 rural homes. A survey of 39 women gathered insights on their use of wood for cooking and perceptions of air quality. This dual approach analyzed both environmental and social dimensions. Findings showed fine particulate matter (0.3, 0.5, 1.0, and 2.5 μm) exceeded safety limits by threefold, while coarser particulates (5.0 and 10 µm) were concerning but less immediate. CO levels were mostly acceptable, but high concentrations posed risks. CO₂ levels indicated good ventilation. Survey responses highlighted reliance on wood and poor air quality perceptions demonstrating little awareness of health risks. Common symptoms included eye discomfort, respiratory issues, and headaches. The study emphasizes the need for interventions to reduce exposure to indoor pollutants and increase awareness of health risks to encourage cleaner cooking practices in rural communities. Full article

This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, including the COVID-19 pre-lockdown, lockdown, and beyond periods, this study used a synergy of in situ and derived satellite time-series data analyses, investigating the daily average inhalable gaseous pollutants ozone (O₃), nitrogen dioxide (NO₂), and particulate matter in two size fractions (PM2.5 and PM10) together with the Air Quality Index (AQI), total Aerosol Optical Depth (AOD) at 550 nm, and climate variables (air temperature at 2 m height, relative humidity, wind speed, and Planetary Boundary Layer height). Applied statistical methods and cross-correlation tests involving multiple datasets of the main air pollutants (inhalable PM2.5 and PM10 and NO₂), AQI, and aerosol loading AOD revealed a direct positive correlation with the spread and severity of COVID-19. Like in other cities worldwide, during the first-wave COVID-19 lockdown, due to the implemented restrictions on human-related emissions, there was a significant decrease in most air pollutant concentrations (PM2.5, PM10, and NO₂), AQI, and AOD but a high increase in ground-level O₃ in all selected metropolises. Also, this study found negative correlations of daily new COVID-19 cases (DNCs) with surface ozone level, air temperature at 2 m height, Planetary Boundary PBL heights, and wind speed intensity and positive correlations with relative humidity. The findings highlight the differential impacts of pandemic lockdowns on air quality in the investigated metropolises. Full article

Improving the powdery mildew resistance of bitter gourd is highly important for achieving high yield and high quality. To better understand the genetic basis of powdery mildew resistance in bitter gourd, this study analyzed 300 lines of recombinant inbred lines (RILs) formed by hybridizing the powdery mildew-resistant material MC18 and the powdery mildew-susceptible material MC402. A high-density genetic map of 1222.04 cM was constructed via incorporating 1,996,505 SNPs generated by resequencing data from 180 lines, and quantitative trait locus (QTL) positioning was performed using phenotypic data at different inoculation stages. A total of seven QTLs related to powdery mildew resistance were identified on four chromosomes, among which qPm-3-1 was detected multiple times and at multiple stages after inoculation. By selecting 18 KASP markers that were evenly distributed throughout the region, 250 lines and parents were genotyped, and the interval was narrowed to 207.22 kb, which explained 13.91% of the phenotypic variation. Through RNA-seq analysis of the parents, 11,868 differentially expressed genes (DEGs) were screened. By combining genetic analysis, gene coexpression, and sequence comparison analysis of extreme materials, two candidate genes controlling powdery mildew resistance in bitter gourd were identified (evm.TU.chr3.2934 (C3H) and evm.TU.chr3.2946 (F-box-LRR)). These results represent a step forward in understanding the genetic regulatory network of powdery mildew resistance in bitter gourd and lay a molecular foundation for the genetic improvement in powdery mildew resistance. Full article

This paper examines the effects of traffic congestion on total crashes, fatal or serious injury (FSI) crashes, and fatal-only crashes in peak periods using a zone-level safety analysis in Greater Melbourne. Bayesian mixed-effect negative binomial models are employed to investigate the relationship between a congestion index and the frequency of total and FSI crashes. In addition, Bayesian mixed-effect binary logistic models are adopted to explore the association between the congestion index and the likelihood of having fatal crashes in Statistical Area Level 2 (SA2) zones. Modelling results indicate that traffic congestion tends to increase total crashes in both the AM and PM peak periods and FSI crashes in the AM peak period. In contrast, traffic congestion tends to decrease the likelihood of having fatal crashes at both the AM and PM peaks. These findings suggest that many policies to reduce traffic congestion may also enhance road safety by lowering the overall number of crashes. However, it is crucial to incorporate careful speed management within these policies to reduce the risk of fatal crashes effectively. Full article

The paper discusses the results of the concentrations of atmospheric particulate matter, in the PM_2.5 and PM₁₀ fractions, acquired by two low-cost sensors. The research was carried out from 1 July 2023 to 30 June 2024, in Palermo, Sicily. The results obtained from two systems equipped with the same sensor model were compared. Excellent linear correlation was observed between the results, with differences in measurements falling within instrumental accuracy. Two instruments equipped with different sensors, models Novasense SDS011 and Plantower PMSA003, were placed at the same site. These were complemented by a weather station to measure meteorological parameters. Upon comparing the atmospheric particulate matter concentrations measured by the two instruments, it was observed that there was a good linear correlation for PM_2.5 and a poor linear correlation for PM₁₀. Additionally, the PMSA003 sensor appeared to consistently record higher concentrations than the SDS011 sensor. During periods influenced by natural sources and/or anthropogenic activities at the regional and/or local scale, i.e., the dispersal of Saharan sands, forest fires, and local events using fireworks, abnormal concentrations of atmospheric particulate matter were detected. Despite the inherent limitations in precision and accuracy, both low-cost instruments were able to identify periods with abnormal concentrations of atmospheric particulate matter, regardless of their source or type. Full article