Material stock analysis of urban road from nighttime light data based on a bottom-up approach

The association between pulmonary tuberculosis recurrence and exposure to fine particulate matter and residential greenness: A population-based retrospective study

Background and objective

To assess the association of pulmonary tuberculosis (PTB) recurrence with fine particulate matter (PM2.5) and residential greenness using a population-based retrospective study design.

Methods

All incident PTB patients, registered in Tuberculosis Information Management System (TBIMS) from 2015 to 2019 in Quzhou City, China, were included. The data on PM2.5 exposure was extracted from the China High Air Pollutants dataset and the level of greenness was estimated using the Normalized Difference Vegetation Index (NDVI) values around the patient's residence. The Cox proportional hazards models were used to quantify the risk of PTB recurrence.

Results

6732 Eligible PTB incident patients were included in the study with a mean age of 56.86 years and a median follow-up time of 750 days. Recurrence was observed in 554 patients (8.2 %). Exposure to NDVI was observed to be negatively associated with PTB recurrence (HR: 0.86, 95 % CI: 0.75-0.98 per 0.1-unit increase). The strength of the association between higher PM2.5 and the risk of PTB recurrence was greater than that of lower PM2.5 concentrations in both low and high NDVI groups (HR:6.62 and 4.35, p-interaction <0.001).

Conclusions

Our findings suggest that higher PM2.5 exposure might increase the risk of PTB recurrence, while residential greenness might have a protective effect. Like other chronic respiratory diseases, prevention and control of PTB will also benefit from comprehensive environmental management.

Association of Fine Particulate Matter and Residential Greenness With Risk of Pulmonary Tuberculosis Retreatment: Population-Based Retrospective Study

Background

The evidence on the association of fine particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) with pulmonary tuberculosis (PTB) retreatment is limited. There are no data on whether greenness exposure protects air pollution-related PTB retreatment in patients with prior PTB.

Objective

In a population-based retrospective study, we aimed to investigate the influence of PM2.5 and residential greenness on the risk of PTB retreatment.

Methods

A total of 26,482 patients with incident PTB, registered in a mandatory web-based reporting system between 2012 and 2019 in Zhengzhou, China, were included in the analysis. The exposure to PM2.5 was assessed based on the China High Air Pollutants dataset, and the level of greenness was estimated using the Normalized Difference Vegetation Index (NDVI) values. The associations of PTB retreatment with exposure to PM2.5 and greenness were evaluated, respectively, considering the local socioeconomic level indicated by the nighttime light index.

Results

Among the 26,482 patients (mean age 46.86, SD 19.52 years) with a median follow-up time of 1523 days per patient, 1542 (5.82%) PTB retreatments were observed between 2012 and 2019. Exposure to PM2.5 was observed to be significantly associated with the increased risk of PTB retreatment in fully adjusted models with a hazard ratio of 1.97 (95% CI 1.34-2.83) per 10 μg/m3 increase in PM2.5. Patients living in the regions with relatively high quartiles of NDVI values had a 45% lower risk of PTB retreatment than those living in the regions with the lowest quartile for the 500 m buffers (hazard ratio 0.55, 95% CI 0.40-0.77). Such a protective effect of residential greenness was more pronounced among patients living in lower nighttime light areas. The strength of the association between PM2.5 exposure and the risk of PTB retreatment was attenuated by greenness. No significant association was observed between NDVI and the incidence of drug resistance.

Conclusions

Long-term exposure to PM2.5 might be a risk factor for PTB retreatment, while an increased level of residential greenness was found to be associated with reduced risks of PTB retreatment. Our results suggest strengthening the control of ambient air pollution and improving residential greenness may contribute to the reduction of PTB retreatment.

Remote sensing estimation of sugar beet SPAD based on un-manned aerial vehicle multispectral imagery

Accurate, non-destructive and cost-effective estimation of crop canopy Soil Plant Analysis De-velopment(SPAD) is crucial for precision agriculture and cultivation management. Unmanned aerial vehicle (UAV) platforms have shown tremendous potential in predicting crop canopy SPAD. This was because they can rapidly and accurately acquire remote sensing spectral data of the crop canopy in real-time. In this study, a UAV equipped with a five-channel multispectral camera (Blue, Green, Red, Red_edge, Nir) was used to acquire multispectral images of sugar beets. These images were then combined with five machine learning models, namely K-Nearest Neighbor, Lasso, Random Forest, RidgeCV and Support Vector Machine (SVM), as well as ground measurement data to predict the canopy SPAD of sugar beets. The results showed that under both normal irrigation and drought stress conditions, the SPAD values in the normal ir-rigation treatment were higher than those in the water-limited treatment. Multiple vegetation indices showed a significant correlation with SPAD, with the highest correlation coefficient reaching 0.60. Among the SPAD prediction models, different models showed high estimation accuracy under both normal irrigation and water-limited conditions. The SVM model demon-strated a good performance with a correlation coefficient (R2) of 0.635, root mean square error (Rmse) of 2.13, and relative error (Re) of 0.80% for the prediction and testing values under normal irrigation. Similarly, for the prediction and testing values under drought stress, the SVM model exhibited a correlation coefficient (R2) of 0.609, root mean square error (Rmse) of 2.71, and rela-tive error (Re) of 0.10%. Overall, the SVM model showed good accuracy and stability in the pre-diction model, greatly facilitating high-throughput phenotyping research of sugar beet canopy SPAD.

Marine litter along the shores of the Persian Gulf, Iran

Plastic wastes -including cigarette butts (CBs)- are dangerous for marine ecosystems not only because they contain hazardous chemicals but also because they can finally turn into micro- or even nano-particles that may be ingested by micro- and macro-fauna. Even large pieces of plastics can trap animals. In this research, the pollution status of macroplastics (abundance, size, type, and colour) and cigarette butts (CBs, number/m2) on the northern coasts of the Persian Gulf has been investigated. A total of 19 stations were explored in Bushehr province (Iran), which covers a length equivalent to 160 km of the Persian Gulf coastline. Among the collected plastic waste (2992 items), disposable mugs were the most frequent (18 %). Plastics with sizes 5-15 cm were the most abundant, and the most common type of plastic was PET (P-value <0.05). The origin of most macroplastics was domestic (2269 items). According to the Index of Clean Coasts (ICC), most surveyed beaches were extremely dirty. The average number and density of CBs in this study were 220 and 2.45 items/m2, respectively. Household litter was the most abundant type of waste in the studied beaches, and this problem can be better managed by training and improving the waste disposal culture. In general, it is suggested that an integrated and enhanced management for fishing, sewage and surface water disposal, and sandy recreational beaches be implemented in Bushehr to control plastic waste.

Spatial analysis of leachate penetration at Lemna dumpsite, Calabar: Implications for sustainable waste management in Cross River State

This study rigorously investigated the spatial analysis of leachate penetration at Lemna dumpsite, located in Calabar, Cross River State, Nigeria. Purposeful soil sampling, performed at specific intervals (5 m, 25 m, and 50 m) along the Electrical Resistivity profile line within the dumpsite, was augmented by water sample collection from five boreholes near Lemna dumpsite. Utilizing Electrical Resistivity Tomography (ERT) and Vertical Electric Sounding (VES) survey techniques, resistivity data were systematically gathered to comprehensively analyze the Leachate Penetration in the Lemna dumpsite. Laboratory analysis of soil and borehole water quality focused on Benzene, Toluene, Ethylbenzene, and Xylene (BTEX), with paired sample t-tests applied for statistical scrutiny. Analyzing the ERT and VES data employed sophisticated techniques embedded in Resistivity Two Dimension Invasion software and Advanced Geosciences Incorporation Earth Imager software. Substantial disparities (p < 0.05) emerged in the paired sample t-tests for BTEX in soil compared to National Environmental Standard Regulation and Enforcement Agency (NESREA) limits. Similarly, BTEX in borehole water displayed significant differences (p < 0.05) when compared to World Health Organization (WHO) standards, raising alarming concerns about the safety and portability of groundwater in the area. The examination of dumpsite leachate penetration revealed a resistivity anomaly of 8.01 Ωm and an inverse depth of 12.4 m, underscoring profound environmental implications and necessitating immediate remediation efforts. Additionally, Vulnerability and Aquifer Protective Capacity Index (VES) results, with a rating of <0.1, indicated severely compromised aquifer protective capacity, emphasizing the vulnerability of groundwater resources to further contamination. Our study advocates for strategic management, remediation, and monitoring measures to prevent contamination and safeguard water quality in the region.