A scoping review of post-earthquake healthcare for vulnerable groups of the 2023 Turkey-Syria earthquakes.

Background: Identifying healthcare services and also strengthening the healthcare systems to effectively deliver them in the aftermath of large-scale disasters like the 2023 Turkey-Syria earthquakes, especially for vulnerable groups cannot be emphasized enough. This study aimed at identifying the interventions undertaken or proposed for addressing the health needs or challenges of vulnerable groups immediately after the occurrence of the 2023 Turkey-Syria earthquakes, as well as for prioritizing their healthcare service delivery in the post-Turkey-Syria earthquake.

Methods: In this scoping review compiled with the five steps of the Arksey and O'Malley framework, five databases, including PubMed, Science Direct, Web of Science, OVID, and Google Scholar, were searched for studies published between March and April 2023 in line with the eligibility criteria.

Public health priorities for Sino-Africa cooperation in Eastern Africa in context of flooding and malaria burden in Children: a tridecadal retrospective analysis.

Background: Malaria remains a major public health burden to children under five, especially in Eastern Africa (E.A), -a region that is also witnessing the increasing occurrence of floods and extreme climate change. The present study, therefore, explored the trends in floods, as well as the association of their occurrence and duration with the malaria incidence in children < 5 years in five E.

Research on Spatial-temporal Spread and Risk Profile of the COVID-19 Epidemic Based on Mobile Phone Trajectory Data.

The COVID-19 epidemic poses a significant challenge to the operation of society and the resumption of work and production. How to quickly track the resident location and activity trajectory of the population, and identify the spread risk of the COVID-19 in geospatial space has important theoretical and practical significance for controlling the spread of the virus on a large scale. In this study, we take the geographical community as the research object, and use the mobile phone trajectory data to construct the spatiotemporal profile of the potential high-risk population.

Mechanical behavior and constitutive model for loess samples under simulated acid rain conditions.

Acid rain is mainly composed of sulfuric acid and nitric acid aqueous solutions, which can deteriorate the mechanical properties of soil and thus threaten the safety of soil engineerings. In this paper, the influence of sulfuric acid rain on mechanical properties of loess soil samples was studied. The diluted sulfuric acid solution has respectively pH 5.

A robust approach to deriving long-term daily surface NO levels across China: Correction to substantial estimation bias in back-extrapolation.

Background: Long-term surface NO data are essential for retrospective policy evaluation and chronic human exposure assessment. In the absence of NO observations for Mainland China before 2013, training a model with 2013-2018 data to make predictions for 2005-2012 (back-extrapolation) could cause substantial estimation bias due to concept drift.

Objective: This study aims to correct the estimation bias in order to reconstruct the spatiotemporal distribution of daily surface NO levels across China during 2005-2018.

The regionalized ecological, economic and social benefit of China's sloping cropland erosion control during the 12 five-year plan (2011-2015).

With Kriging interpolation, analytic hierarchy process and grey relational analysis, this paper evaluates the regionalized benefit of China's sloping cropland erosion control (SCEC) during 2011-2015, including the ecological, economic, social benefit and the comprehensive benefit. The results show that, in the ecological benefits, the distribution of soil erosion control degree presents patchy characteristics. The reduction of runoff modulus gradually decreases from southeast to northwest.

Association between ambient temperature and hypertensive disorders in pregnancy in China.

Hypertensive disorders in pregnancy (HDPs) are leading perinatal diseases. Using a national cohort of 2,043,182 pregnant women in China, we evaluated the association between ambient temperatures and HDP subgroups, including preeclampsia or eclampsia, gestational hypertension, and superimposed preeclampsia. Under extreme temperatures, very cold exposure during preconception (12 weeks) increases odds of preeclampsia or eclampsia and gestational hypertension.

Spatiotemporal distributions of ambient SO across China based on satellite retrievals and ground observations: Substantial decrease in human exposure during 2013-2016.

Multiyear spatiotemporal distributions of daily ambient sulfur dioxide (SO) are essential for evaluating management effectiveness and assessing human health risk. In this study, we estimate the daily SO levels across China on 0.1 grid from 2013 to 2016 by assimilating satellite- and ground-based SO observations using the random-forest spatiotemporal kriging (RF-STK) model.

Assessing Susceptibility of Debris Flow in Southwest China Using Gradient Boosting Machine.

A gradient boosting machine (GBM) was developed to model the susceptibility of debris flow in Sichuan, Southwest China for risk management. A total of 3839 events of debris flow during 1949-2017 were compiled from the Sichuan Geo-Environment Monitoring program, field surveys, and satellite imagery interpretation. In the cross-validation, the GBM showed better performance, with the prediction accuracy of 82.

A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM levels.

Satellite-retrieved aerosol optical depth (AOD) is commonly used to estimate ambient levels of fine particulate matter (PM), though it is important to mitigate the estimation bias of PM due to gaps in satellite-retrieved AOD. A nonparametric approach with two random-forest submodels is proposed to estimate PM levels by filling gaps in satellite-retrieved AOD. This novel approach was employed to estimate the spatiotemporal distribution of daily PM levels during 2013-2015 in the Sichuan Basin of Southwest China, where the coverage rate of composite AOD retrieved by the Terra and Aqua satellites was only 11.

Satellite-Based Estimates of Daily NO Exposure in China Using Hybrid Random Forest and Spatiotemporal Kriging Model.

A novel model named random-forest-spatiotemporal-kriging (RF-STK) was developed to estimate the daily ambient NO concentrations across China during 2013-2016 based on the satellite retrievals and geographic covariates. The RF-STK model showed good prediction performance, with cross-validation R = 0.62 (RMSE = 13.

Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment.

In China, ozone pollution shows an increasing trend and becomes the primary air pollutant in warm seasons. Leveraging the air quality monitoring network, a random forest model is developed to predict the daily maximum 8-h average ozone concentrations ([O]) across China in 2015 for human exposure assessment. This model captures the observed spatiotemporal variations of [O] by using the data of meteorology, elevation, and recent-year emission inventories (cross-validation R = 0.

A fuel-based approach for emission factor development for highway paving construction equipment in China.

Unlabelled: The objective of this paper is to develop and demonstrate a fuel-based approach for emissions factor estimation for highway paving construction equipment in China for better accuracy. A highway construction site in Chengdu was selected for this study with NO emissions being characterized and demonstrated. Four commonly used paving equipment, i.

Local farmers' perceptions of climate change and local adaptive strategies: a case study from the Middle Yarlung Zangbo River Valley, Tibet, China.

Climate change affects the productivity of agricultural ecosystems. Farmers cope with climate change based on their perceptions of changing climate patterns. Using a case study from the Middle Yarlung Zangbo River Valley, we present a new research framework that uses questionnaire and interview methods to compare local farmers' perceptions of climate change with the adaptive farming strategies they adopt.