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. Interventions for enhancing post-earthquake healthcare services (PEHS) were grouped into seven (7) categories, adopted from previous guidelines and studies. Each one was assigned a default score of a value equal to one (1), which, in the end, was summed up. RESULTS: Of the 115 total records initially screened, 29 articles were eligible for review. Different interventions they reported either undertaken or proposed to address the healthcare needs and challenges, especially faced by the most vulnerable groups in the aftermath of the Turkey-Syria earthquakes, were categorized into 7 PEHS. They were ranked with their scores as follows: humanitarian health relief (25); medical care (17); mental health and psychosocial support (10); health promotion, education, and awareness (9); disease surveillance and prevention (7); disability rehabilitation (7); and sexual and reproductive health (5). CONCLUSION: Since there are no proper guidelines or recommendations about the specific or most significant PEHS to prioritize for vulnerable groups after the occurrence of large-scale earthquakes, this scoping review provides some insights that can help inform healthcare service delivery and prioritization for vulnerable groups in the post-2023 Turkey-Syria earthquakes and other similar disasters.

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.A partner countries of Forum for China-Africa Cooperation (FOCAC), including Ethiopia, Kenya, Somalia, Sudan, and Tanzania between 1990 and 2019. METHODS: A retrospective analysis of data retrieved from two global sources was performed: the Emergency Events Database (EM-DAT) and the Global Burden of Diseases Study (GBD) between 1990 and 2019. Using SPSS 20.0, a correlation was determined based on ρ= -1 to + 1, as well as the statistical significance of P = < 0.05. Time plots of trends in flooding and malaria incidence were generated in 3 different decades using R version 4.0. RESULTS: Between 1990 and 2019, the occurrence and duration of floods among the five E.A partner countries of FOCAC increased and showed an upward trend. On the contrary, however, this had an inverse and negative, as well as a weak correlation on the malaria incidence in children under five years. Only Kenya, among the five countries, showed a perfect negative correction of malaria incidence in children under five with flood occurrence (ρ = -0.586**, P-value = 0.001) and duration (ρ = -0.657**, P-value = < 0.0001). CONCLUSIONS: This study highlights the need for further research to comprehensively explore how different climate extreme events, which oftentimes complement floods, might be influencing the risk of malaria in children under five in five E.A malaria-endemic partner countries of FOCAC. Similarly, it ought to consider investigating the influence of other attributes apart from flood occurrence and duration, which also compound floods like displacement, malnutrition, and water, sanitation and hygiene on the risk and distribution of malaria and other climate-sensitive diseases.

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. First, by using the spatiotemporal data collision method, identify, and recover the trajectories of the people who were in the same area with the confirmed patients during the same time. Then, based on the range of activities of both cohorts (the confirmed cases and the potentially infected groups), we analyze the risk level of the relevant places and evaluate the scale of potential spread. Finally, we calculate the probability of infection for different communities and construct the spatiotemporal profile for the transmission to help guide the distribution of preventive materials and human resources. The proposed method is verified using survey data of 10 confirmed cases and statistical data of 96 high-risk neighborhoods in Chengdu, China, between 15 January 2020 and 15 February 2020. The analysis finds that the method accurately simulates the spatiotemporal spread of the epidemic in Chengdu and measures the risk level in specific areas, which provides an objective basis for the government and relevant parties to plan and manage the prevention and control of the epidemic.

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 5.0, 4.0 and 3.0 to simulate the acid rain condition, and the triaxial compressional tests and scanning electron microscope were carried out to investigate the deteriorated properties and evolution of the microstructure of the saturated loess samples. The results demonstrated that acid rain made the porosity of loess samples larger, and changed the pore distribution and contacts of soil grains, so that the mechanical properties of loess samples varied in some degree. With the decrease of pH value, both the peak value of the deviatoric stress and volumetric contraction of loess samples decreased, which reduced the parameters of shear strength of loess samples. Furthermore, a framework of the chemical-mechanical model for loess under the action of acid rain was established, in which loess was considered as a porous medium material, and the influences of acid rain with different pH values were taken into account in the double-hardening constitutive model, and the model was also verified by the triaxial test results finally.

A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation.

BACKGROUND: Long-term surface NO2 data are essential for retrospective policy evaluation and chronic human exposure assessment. In the absence of NO2 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 NO2 levels across China during 2005-2018. METHODS: On the basis of ground- and satellite-based data, we proposed the robust back-extrapolation with a random forest (RBE-RF) to simulate the surface NO2 through intermediate modeling of the scaling factors. For comparison purposes, we also employed a random forest (Base-RF), as a representative of the commonly used approach, to directly model the surface NO2 levels. RESULTS: The validation against Taiwan's NO2 observations during 2005-2012 showed that RBE-RF adequately corrected the substantial underestimation by Base-RF. The RMSE decreased from 10.1 to 8.2 µg/m3, 7.1 to 4.3 µg/m3, and 6.1 to 2.9 µg/m3 in predicting daily, monthly, and annual levels, respectively. For North China with the most severe pollution, the population-weighted NO2 ([NO2]pw) during 2005-2012 was estimated as 40.2 and 50.9 µg/m3 by Base-RF and RBE-RF, respectively, i.e., 21.0% difference. While both models predicted that the national annual [NO2]pw increased during 2005-2011 and then decreased, the interannual trends were underestimated by >50.2% by Base-RF relative to RBE-RF. During 2005-2018, the nationwide population that lived in the areas with NO2 > 40 µg/m3 were estimated as 259 and 460 million by Base-RF and RBE-RF, respectively. CONCLUSION: With RBE-RF, we corrected the estimation bias in back-extrapolation and obtained a full-coverage dataset of daily surface NO2 across China during 2005-2018, which is valuable for environmental management and epidemiological research.

The regionalized ecological, economic and social benefit of China's sloping cropland erosion control during the 12th 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. The reduction of soil erosion modulus is the largest in the Northwest Loess Plateau and the smallest in the Northeast Black Soil Zone. In the economic benefits, the increase in the annual output value per unit land area is characterized by "high in the south and low in the north", but there are patchy high-value areas in central Loess Plateau and the Northern Earthy-Rocky Mountain Zone. The increase in the agricultural population's per capita income is higher in the western area than that in the eastern area. In the social benefits, the per capita grain increase in most of the northern China is larger than that in the south, while the characteristic agricultural development in the south is more advantageous than that in the north. The comprehensive benefit is "high in the south and low in the north; highest in the southwest and lowest in the northeast". The spatial heterogeneity implies the necessity to specify the influencing factors for the SCEC benefit in different areas and take pointed measures to improve the benefit.

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. Compared to preconception, in the first half of pregnancy, the impact of temperature on preeclampsia or eclampsia and gestational hypertension is opposite. Cold exposure decreases the odds, whereas hot exposure increases the odds. Under average temperatures, a temperature increase during preconception decreases the risk of preeclampsia or eclampsia and gestational hypertension. However, in the first half of pregnancy, temperature is positively associated with a higher risk. No significant association is observed between temperature and superimposed preeclampsia. Here we report a close relationship exists between ambient temperature and preeclampsia or eclampsia and gestational hypertension.

Spatiotemporal distributions of ambient SO2 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 (SO2) are essential for evaluating management effectiveness and assessing human health risk. In this study, we estimate the daily SO2 levels across China on 0.1o grid from 2013 to 2016 by assimilating satellite- and ground-based SO2 observations using the random-forest spatiotemporal kriging (RF-STK) model. The cross-validation R2 is 0.64 and 0.81 for predicting the daily and multiyear averages, respectively. The multiyear population-weighted average of SO2 for China is 28.1 ±â€¯14.0 µg/m3, and the severest SO2 pollution occurs in the northern China (45.1 ±â€¯14.7 µg/m3). The SO2 concentration shows a strong seasonality, i.e., highest in winter (41.6 ±â€¯26.4 µg/m3) and lowest in summer (19.6 ±â€¯8.3 µg/m3). During 2013-2016, the annual SO2 decreases from 34.4 ±â€¯18.2 to 22.7 ±â€¯11.1 µg/m3, and the population% exposed for more than 100 nonattainment days (SO2 > 20 µg/m3) drops from 86% to 48%. While the seasonality of SO2 is mainly determined by the meteorological variation, the substantial decrease attributes to the reduced emissions such as from coal consumption. The effectiveness of SO2 emission reduction varies widely in different prefectures of China. In Shandong province, the SO2 concentration decreases by -45% while the coal consumption increases by 9%. In Shanxi province, the SO2 concentration decreases by -15% while the coal consumption decreases by -3%. The contrasting effectiveness between these two provinces is associated with the much fewer waste gas disposal facilities in Shanxi than Shandong. Stricter regulation is required to further lower the SO2 concentration in order to protect the public health, especially in the northern China.

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 82.0% and area under curve of 0.88, than the benchmark models, including the Logistic Regression, the K-Nearest Neighbor, the Support Vector Machine, and the Artificial Neural Network. The elevation range, precipitation, and aridity index played the most important role in determining the susceptibility. In addition, the water erosion intensity, road construction, channel gradient, and human settlement sites also largely contributed to the formation of debris flow. The susceptibility map produced by the GBM shows that the spatial distributions of high-susceptibility watersheds were highly coupled with the locations of the topographical extreme belt, fault zone, seismic belt, and dry valleys. This study provides critical information for risk mitigating and prevention of debris flow.

A nonparametric approach to filling gaps in satellite-retrieved aerosol optical depth for estimating ambient PM2.5 levels.

Satellite-retrieved aerosol optical depth (AOD) is commonly used to estimate ambient levels of fine particulate matter (PM2.5), though it is important to mitigate the estimation bias of PM2.5 due to gaps in satellite-retrieved AOD. A nonparametric approach with two random-forest submodels is proposed to estimate PM2.5 levels by filling gaps in satellite-retrieved AOD. This novel approach was employed to estimate the spatiotemporal distribution of daily PM2.5 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 11.7%. Based on the retrieved AOD and various covariates (including meteorological conditions and land use types), the first random-forest submodel (named AOD-submodel) was trained to fill the gaps in the AOD dataset, giving a cross-validation R2 of 0.95. Subsequently, the second random-forest submodel (named PM2.5-submodel) was trained to estimate the PM2.5 levels for unmonitored areas/days based on the gap-filled AOD, ground-monitored PM2.5 levels, and the covariates, and achieved a cross-validation R2 of 0.86. By comparing the complete and incomplete (i.e., without the days when AOD data were missing) estimates, we found that the monthly PM2.5 levels could be overestimated by 34.6% if the PM2.5 values coincident with AOD gaps were not considered. The newly developed approach is valuable for deriving the complete spatiotemporal distribution of daily PM2.5 from incomplete remote-sensing data, which is essential for air quality management and human exposure assessment.

Satellite-Based Estimates of Daily NO2 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 NO2 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 R2 = 0.62 (RMSE = 13.3 µg/m3) for daily and R2 = 0.73 (RMSE = 6.5 µg/m3) for spatial predictions. The nationwide population-weighted multiyear average of NO2 was predicted to be 30.9 ± 11.7 µg/m3 (mean ± standard deviation), with a slowly but significantly decreasing trend at a rate of -0.88 ± 0.38 µg/m3/year. Among the main economic zones of China, the Pearl River Delta showed the fastest decreasing rate of -1.37 µg/m3/year, while the Beijing-Tianjin Metro did not show a temporal trend ( P = 0.32). The population-weighted NO2 was predicted to be the highest in North China (40.3 ± 10.3 µg/m3) and lowest in Southwest China (24.9 ± 9.4 µg/m3). Approximately 25% of the population lived in nonattainment areas with annual-average NO2 > 40 µg/m3. A piecewise linear function with an abrupt point around 100 people/km2 characterized the relationship between the population density and the NO2, indicating a threshold of aggravated NO2 pollution due to urbanization. Leveraging the ground-level NO2 observations, this study fills the gap of statistically modeling nationwide NO2 in China, and provides essential data for epidemiological research and air quality management.

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 ([O3]MDA8) across China in 2015 for human exposure assessment. This model captures the observed spatiotemporal variations of [O3]MDA8 by using the data of meteorology, elevation, and recent-year emission inventories (cross-validation R2 = 0.69 and RMSE = 26 µg/m3). Compared with chemical transport models that require a plenty of variables and expensive computation, the random forest model shows comparable or higher predictive performance based on only a handful of readily-available variables at much lower computational cost. The nationwide population-weighted [O3]MDA8 is predicted to be 84 ± 23 µg/m3 annually, with the highest seasonal mean in the summer (103 ± 8 µg/m3). The summer [O3]MDA8 is predicted to be the highest in North China (125 ± 17 µg/m3). Approximately 58% of the population lives in areas with more than 100 nonattainment days ([O3]MDA8>100 µg/m3), and 12% of the population are exposed to [O3]MDA8>160 µg/m3 (WHO Interim Target 1) for more than 30 days. As the most populous zones in China, the Beijing-Tianjin Metro, Yangtze River Delta, Pearl River Delta, and Sichuan Basin are predicted to be at 154, 141, 124, and 98 nonattainment days, respectively. Effective controls of O3 pollution are urgently needed for the highly-populated zones, especially the Beijing-Tianjin Metro with seasonal [O3]MDA8 of 140 ± 29 µg/m3 in summer. To the best of the authors' knowledge, this study is the first statistical modeling work of ambient O3 for China at the national level. This timely and extensively validated [O3]MDA8 dataset is valuable for refining epidemiological analyses on O3 pollution in China.

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

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.e., three rollers and one paver were selected in this study. A portable emission measurement system (PEMS) was developed and used for emission measurements of selected equipment during real-world highway construction duties. Three duty modes were defined to characterize the NO emissions, i.e., idling, moving, and working. In order to develop a representative emission factor for these highway construction equipment, composite emission factors were estimated using modal emission rates and the corresponding modal durations in the process of typical construction duties. Depending on duty mode and equipment type, NO emission rate ranged from 2.6-63.7mg/s and 6.0-55.6g/kg-fuel with the fuel consumption ranging from 0.31-4.52 g/s correspondingly. The NO composite emission factor was estimated to be 9-41mg/s with the single-drum roller being the highest and double-drum roller being the lowest and 6-30g/kg-fuel with the pneumatic tire roller being the highest while the double-drum roller being the lowest. For the paver, both time-based and fuel consumption-based NO composite emission rates are higher than all of the rollers with 56mg/s and 30g/kg-fuel, respectively. In terms of time-based quantity, the working mode contributes more than the other modes with idling being the least for both emissions and fuel consumption. In contrast, the fuel-based emission rate appears to have less variability in emissions. Thus, in order to estimate emission factors for emission inventory development, the fuel-based emission factor may be selected for better accuracy. IMPLICATIONS: The fuel-based composite emissions factors will be less variable and more accurate than time-based emission factors. As a consequence, emissions inventory developed using this approach will be more accurate and practical.

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. Most farmers in the valley believed that temperatures had increased in the last 30 years but did not note any changes in precipitation. Most farmers also reported sowing and harvesting hulless barley 10-15 days earlier than they were 20 years ago. In addition, farmers observed that plants were flowering and river ice was melting earlier in the season, but they did not perceive changes in plant germination, herbaceous vegetation growth, or other spring seasonal events. Most farmers noticed an extended fall season signified by delays in the freezing of rivers and an extended growing season for grassland vegetation. The study results showed that agricultural practices in the study area are still traditional; that is, local farmers' perceptions of climate change and their strategies to mitigate its impacts were based on indigenous knowledge and their own experiences. Adaptive strategies included adjusting planting and harvesting dates, changing crop species, and improving irrigation infrastructure. However, the farmers' decisions could not be fully attributed to their concerns about climate change. Local farming systems exhibit high adaptability to climate variability. Additionally, off-farm income has reduced the dependence of the farmers on agriculture, and an agricultural subsidy from the Chinese Central Government has mitigated the farmers' vulnerability. Nevertheless, it remains necessary for local farmers to build a system of adaptive climate change strategies that combines traditional experience and indigenous knowledge with scientific research and government polices as key factors.