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PM2.5, fine particulate matter with a diameter smaller than 2.5 μ m , is associated with a range of health problems. Monitoring PM2.5 levels at the community scale is crucial for understanding personal exposure and implementing preventive measures. While monitoring agencies around the world, such as the U.S. Environmental Protection Agency (EPA), provide accurate data, the spatial coverage is limited due to a sparse monitoring network. Recently, the emergence of low-cost air quality sensor networks has enabled the availability of air quality data with higher spatiotemporal resolution, which is more representative of personal exposure. However, concerns persist regarding the sensitivity, noise, and reliability of data from these low-cost sensors. In this study, we analyzed PM2.5 data from both EPA and Purple Air (PA) sensors in Cook County, Illinois, with two primary goals: (1) understanding the differential impact of meteorological factors on PA and EPA sensor networks and (2) provide a mathematical approach to quantify the individual impact of correlated predictors on both short-term and baseline variations in noisy time series data. We used the Kolmogorov-Zurbenko (KZ) filter to separate the time series into short-term and baseline components, followed by fitting linear models to quantify the impact of meteorological predictors, including temperature, relative humidity (RH), wind speed (WS), and wind direction (WD). Furthermore, we applied the Lindeman, Merenda, and Gold (LMG) method to these linear models to quantify the individual contribution of each predictor in the presence of multicollinearity. Our results show that the PM2.5 data from PA sensors exhibit higher sensitivity to meteorological factors, particularly wind speed, in the short-term and RH in the baseline component. This method provides a structured approach for analyzing noisy sensor data under diverse environmental conditions. [ABSTRACT FROM AUTHOR]

With global warming and the gradual melting of Arctic sea ice, the navigation duration of the Northeast Passage (NEP) is gradually increasing. The dynamic changes in sea ice concentration (SIC) during navigation time are a critical factor affecting the navigation of the passage. This study uses multiple linear regression and random forest to analyze the navigation windows of the NEP from 1979 to 2022 and examines the critical factors affecting the dynamic changes in the SIC. The results suggest that there are 25 years of navigable windows from 1979 to 2022. The average start date of navigable windows is approximately between late July and early August, while the end date is approximately early and mid-October, with considerable variation in the duration of navigable windows. The explanatory power of RF is significantly better than MLR, while LMG is better at identifying extreme events, and RF is more suitable for assessing the combined effects of all variables on the sea ice concentration. This study also found that the 2 m temperature is the main influencing factor, and the sea ice movement, sea level pressure and 10 m wind speed also play a role in a specific period. By integrating traditional statistical methods with machine learning techniques, this study reveals the dynamic changes of the SIC during the navigation period of the NEP and identifies its driving factors. This provides a scientific reference for the development and utilization of the Arctic Passage. [ABSTRACT FROM AUTHOR]

PM2.5, fine particulate matter with a diameter smaller than 2.5 μm, is associated with a range of health problems. Monitoring PM2.5 levels at the community scale is crucial for understanding personal exposure and implementing preventive measures. While monitoring agencies around the world, such as the U.S. Environmental Protection Agency (EPA), provide accurate data, the spatial coverage is limited due to a sparse monitoring network. Recently, the emergence of low-cost air quality sensor networks has enabled the availability of air quality data with higher spatiotemporal resolution, which is more representative of personal exposure. However, concerns persist regarding the sensitivity, noise, and reliability of data from these low-cost sensors. In this study, we analyzed PM2.5 data from both EPA and Purple Air (PA) sensors in Cook County, Illinois, with two primary goals: (1) understanding the differential impact of meteorological factors on PA and EPA sensor networks and (2) provide a mathematical approach to quantify the individual impact of correlated predictors on both short-term and baseline variations in noisy time series data. We used the Kolmogorov-Zurbenko (KZ) filter to separate the time series into short-term and baseline components, followed by fitting linear models to quantify the impact of meteorological predictors, including temperature, relative humidity (RH), wind speed (WS), and wind direction (WD). Furthermore, we applied the Lindeman, Merenda, and Gold (LMG) method to these linear models to quantify the individual contribution of each predictor in the presence of multicollinearity. Our results show that the PM2.5 data from PA sensors exhibit higher sensitivity to meteorological factors, particularly wind speed, in the short-term and RH in the baseline component. This method provides a structured approach for analyzing noisy sensor data under diverse environmental conditions.

With global warming and the gradual melting of Arctic sea ice, the navigation duration of the Northeast Passage (NEP) is gradually increasing. The dynamic changes in sea ice concentration (SIC) during navigation time are a critical factor affecting the navigation of the passage. This study uses multiple linear regression and random forest to analyze the navigation windows of the NEP from 1979 to 2022 and examines the critical factors affecting the dynamic changes in the SIC. The results suggest that there are 25 years of navigable windows from 1979 to 2022. The average start date of navigable windows is approximately between late July and early August, while the end date is approximately early and mid-October, with considerable variation in the duration of navigable windows. The explanatory power of RF is significantly better than MLR, while LMG is better at identifying extreme events, and RF is more suitable for assessing the combined effects of all variables on the sea ice concentration. This study also found that the 2 m temperature is the main influencing factor, and the sea ice movement, sea level pressure and 10 m wind speed also play a role in a specific period. By integrating traditional statistical methods with machine learning techniques, this study reveals the dynamic changes of the SIC during the navigation period of the NEP and identifies its driving factors. This provides a scientific reference for the development and utilization of the Arctic Passage.

Blood is important evidence that can assist an investigator in solving a crime. It connects the suspect to a crime and aid in the reconstruction of the crime scene. Criminals frequently attempt to wipe away blood stained evidence at a crime scene. These efforts may result in the modification or partial or total elimination of blood on the stained parts. Numerous presumptive tests are employed to identify bloodstains on floor surfaces since they are frequently cleansed after a crime using various surfactants. However, the traces of stains remain there and if analysed with appropriate reagents will give conclusive results. In This study Blood-stained floor surfaces were cleaned using a commercially available and widely used floor cleaners and After repeated washings, the Kastle-Meyer (KM), Leucomalachite green (LMG), and Tetra methylbenzidine (TMB) tests were employed to determine the presence of blood on these surfaces, and their sensitivity were assessed, this study will help the forensic investigator to select the appropriate reagents for detection of blood stains on the various washed floor surfaces and effectiveness of reagents for detection of blood stains washed with various floor cleaners. [ABSTRACT FROM AUTHOR]

The presented study evaluated a set of beads primarily originating from the Hallstatt period (800–400 BC) and uncovered in the region of Bohemia. Utilizing an SEM/EDS method, the chemical composition of the glass samples was determined and their homogeneity measured. Owing to the presence of opaque glass, Raman spectroscopy was applied, enabling the definition of the phases causing the opacity of the glass, as well as its coloring. This article discusses opacifying agents, including the possible ways in which they entered the artefacts. In addition, the techniques used to produce the glass beads are described, for both the single-colored beads, as well as the so-called eye beads that are present in a significant amount in the set. The majority of the beads examined were found to be made of the LMG glass type (low-magnesium soda-lime glass). An unexpected result was the identification of glass with a high content of K2O not corresponding to the mixed alkali type (LMHK), which is frequently discussed in the literature. The glass type in question most likely does not come from the traditional area of glass production: the eastern Mediterranean territory. [ABSTRACT FROM AUTHOR]

Poverty affects many people worldwide and varies in space and time, although its determinants are geographical factors. This paper presents a case study from Hubei Province, Central China, investigating the spatial and temporal changes in poverty determinants at the county from 2013-2019 and village levels from 2013 to 2017. We investigated the variation in the spatial autocorrelation of poverty incidence at the two levels using global and local Moran's I. We then explored the spatial and temporal variations of poverty determinants using the Lineman, Merenda, and Gold method. We found that the overall spatial autocorrelation gradually mitigated, whereas the local spatial pattern remained unchanged at both levels. Deeply poor areas were concentrated in the western part of Hubei Province and the southwestern part of Yunyang County. The effects of geographical conditions on poverty decreased across the study period, with the R2 value decreasing from 85% to 73% at the county level and from 57% to 38% at the village level. Furthermore, the contribution of natural environmental factors to poverty slightly decreased at both scale levels, whereas the socioeconomic factors had a significantly increased effect on county-level poverty over time. By contrast, the factors that have a major effect on village-level poverty remained stable. The results might indicate that the implementation of various targeted poverty alleviation measures since 2013 have mitigated the restrictions of local geographical factors on poverty alleviation. [ABSTRACT FROM AUTHOR]