Study on the forecasting and maintenance system of special railway subsidence in mine area

The fully mechanized caving coal mining under the railway in mine area will result in difficulty maintenance of railway because of great distortion and subsidence speed of terrene and railway. If the subsidence forecasting is incorrect and maintenance measure is not suitable in the preceding and the process of mining, the normal operation of the railway in mine area will not be ensured and perhaps the safety accident will be resulted. The railway subsidence forecasting and maintenance system for fully mechanized caving coal face are studied and developed in this connection. Based on the accurate subsidence forecasting of the terrene and railway, the maintenance measure for track and switch turnout in railway is put forward in this system.

Ecological-Based Mining:A Coal-Water-Thermal Collaborative Paradigm in Ecologically Fragile Areas in Western China

A substantial reduction in groundwater level,exacerbated by coal mining activities,is intensifying water scarcity in western China’s ecologically fragile coal mining areas.China’s national strategic goal of achieving a carbon peak and carbon neutrality has made eco-friendly mining that prioritizes the protection and efficient use of water resources essential.Based on the resource characteristics of mine water and heat hazards,an intensive coal-water-thermal collaborative co-mining paradigm for the duration of the mining process is proposed.An integrated system for the production,supply,and storage of mining companion resources is achieved through technologies such as roof water inrush prevention and control,hydrothermal quality improvement,and deep-injection geological storage.An active preventive and control system achieved by adjusting the mining technology and a passive system centered on multiobjective drainage and grouting treatment are suggested,in accordance with the original geological characteristics and dynamic process of water inrush.By implementing advanced multi-objective drainage,specifically designed to address the“skylight-type”water inrush mode in the Yulin mining area of Shaanxi Province,a substantial reduction of 50%in water drillings and inflow was achieved,leading to stabilized water conditions that effectively ensure subsequent safe coal mining.An integrated-energy complementary model that incorporates the clean production concept of heat utilization is also proposed.The findings indicate a potential saving of 8419 t of standard coal by using water and air heat as an alternative heating source for the Xiaojihan coalmine,resulting in an impressive energy conservation of 50.2%and a notable 24.2%reduction in carbon emissions.The ultra-deep sustained water injection of 100 m^(3)·h^(-1)in a single well would not rupture the formation or cause water leakage,and 7.87×10^(5)t of mine water could be effectively stored in the Liujiagou Formation,presenting a viable method for mine-water management in the Ordos Basin and providing insights for green and low-carbon mining.

A Deep Learning Approach for Forecasting Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China,and it is of great importance to correctly forecast them.At present,the forecasting of thunderstorm gusts is mainly based on traditional subjective methods,which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources.In this paper,we propose a deep learning method called Thunderstorm Gusts TransU-net(TGTransUnet)to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology(IUM)with a lead time of 1 to 6 h.To determine the specific range of thunderstorm gusts,we combine three meteorological variables:radar reflectivity factor,lightning location,and 1-h maximum instantaneous wind speed from automatic weather stations(AWSs),and obtain a reasonable ground truth of thunderstorm gusts.Then,we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture,which is based on convolutional neural networks and a transformer.The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training,validation,and testing datasets.Finally,the performance of TG-TransUnet is compared with other methods.The results show that TG-TransUnet has the best prediction results at 1–6 h.The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China.

Harmony between Industrial Landscape and Natural Ecology—Landscape Design of the Former Area in Wangfenggang Coal Mine of Anhui Huainan

Taking landscape design of the former area in Wangfenggang coal mine of Anhui Huainan as an example,the whole thinking and specific implementations were discussed from aspects including scenic spot planning,plant landscape and characteristics of scenic spots,and created landscape in mining areas,squares and residential districts by combining section peculiarity,old building characteristics and circumstance in the landscape design of mining areas,which aimed to realize sustainable development of landscape in mining areas.

Research on the Relationship Between Environmental and Economic Coupling Systems in Bohai Bay Area Based on a Vector Autoregression(VAR)Model

This study analyzed the impact of land-based contaminants and tertiary industrial structure on economic development in the selected Bohai Bay area,China.Based on panel data spanning 2011-2020,a vector autoregressive(VAR)model is used to analyze and forecast the short-run and long-run relationships between three industrial structures,pollutant discharge,and economic development.The results showed that the environmental index had a long-term cointegration relationship with the industrial structure economic index.Per capital chemical oxygen demand(PCOD)and per capita ammonia nitrogen(PNH_(3)N)had a positive impact on delta per capita GDP(dPGDP),while per capita solid waste(PSW),the secondary industry rate(SIR)and delta tertiary industry(dTIR)had a negative impact on dPGDP.The VAR model under this coupling system had stability and credibility.The impulse response results showed that the short-term effect of the coupling system on dPGDP was basically consistent with the Granger causality test results.In addition,variance decomposition was used in this study to predict the long-term impact of the coupling system in the next ten periods(i.e.,ten years).It was found that dTIR had a great impact on dPGDP,with a contribution rate as high as 74.35%in the tenth period,followed by the contribution rate of PCOD up to 3.94%,while the long-term contribution rates of PSW,SIR and PNH3N were all less than 1%.The results show that the government should support the development of the tertiary industry to maintain the vitality of economic development and prevent environmental deterioration.

Seasonal Characteristics of Forecasting Uncertainties in Surface PM_(2.5)Concentration Associated with Forecast Lead Time over the Beijing-Tianjin-Hebei Region

Forecasting uncertainties among meteorological fields have long been recognized as the main limitation on the accuracy and predictability of air quality forecasts.However,the particular impact of meteorological forecasting uncertainties on air quality forecasts specific to different seasons is still not well known.In this study,a series of forecasts with different forecast lead times for January,April,July,and October of 2018 are conducted over the Beijing-Tianjin-Hebei(BTH)region and the impacts of meteorological forecasting uncertainties on surface PM_(2.5)concentration forecasts with each lead time are investigated.With increased lead time,the forecasted PM_(2.5)concentrations significantly change and demonstrate obvious seasonal variations.In general,the forecasting uncertainties in monthly mean surface PM_(2.5)concentrations in the BTH region due to lead time are the largest(80%)in spring,followed by autumn(~50%),summer(~40%),and winter(20%).In winter,the forecasting uncertainties in total surface PM_(2.5)mass due to lead time are mainly due to the uncertainties in PBL heights and hence the PBL mixing of anthropogenic primary particles.In spring,the forecasting uncertainties are mainly from the impacts of lead time on lower-tropospheric northwesterly winds,thereby further enhancing the condensation production of anthropogenic secondary particles by the long-range transport of natural dust.In summer,the forecasting uncertainties result mainly from the decrease in dry and wet deposition rates,which are associated with the reduction of near-surface wind speed and precipitation rate.In autumn,the forecasting uncertainties arise mainly from the change in the transport of remote natural dust and anthropogenic particles,which is associated with changes in the large-scale circulation.

Promising Results Predict Role for Artificial Intelligence in Weather Forecasting

Artificial intelligence(AI)has already demonstrated its proficiency at difficult scientific tasks like predicting how proteins will fold and identifying new astronomical objects in masses of observational data[1].Now,recent results suggest that AI also excels at weather forecasting.For global predictions,GraphCast,an AI system developed by Google subsidiary DeepMind(London,UK),outperforms the state-of-the-art model from the European Centre for Medium-Range Weather Forecasts(ECMWF),providing more accurate projections of variables such as temperature and humidity 90%of the time[2,3].Other AI systems,including Pangu-Weather from the Chinese tech company Huawei(Shenzhen,China)[4],can also match or beat traditional global forecasting models.

Diversity of Orchids in Terms of Their Distribution, Uses and Conservation in Annapurna Conservation Area of Nepal

The Annapurna Conservation Area (ACA), the first conservation area and the largest protected area (PA) in Nepal, is incredibly rich in biodiversity. Notwithstanding this, orchids in the ACA have not been explored enough yet thus making the need for ambitious research to be carried out. Previous study only included 81 species of orchids within ACA. This study aims to update the record of species and genera richness in the ACA. In total 198 species of orchids, belonging to 67 genera (40% and 62% of the total recorded orchid species and genera in Nepal) has been recorded in ACA. This represents an increase of 144% in species and 56% in genera over the previous data. Out of the 198 species, 99 were epiphytes, 6 were holomycotrophic and 93 were terrestrial. Among the 67 genera, Bulbophyllum (17) species were dominant, followed by Dendrobium (16), Herminium (10), Coelogyne, Plantanthera (9 each), Eria, Habenaria, Oberonia (8 each), Calanthe (7), and Liparis (6). Fifty-six species were found to be ornamentally significant and 85 species medicinally significant.

Scientific Advances and Weather Services of the China Meteorological Administration’s National Forecasting Systems during the Beijing 2022 Winter Olympics

Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.

The environmental analysis and site selection of mussel and large yellow croaker aquaculture areas based on high resolution remote sensing

Mussel aquaculture and large yellow croaker aquaculture areas and their environmental characteristics in Zhoushan were analyzed using satellite data and in-situ surveys.A new two-step remote sensing method was proposed and applied to determine the basic environmental characteristics of the best mussel and large yellow croaker aquaculture areas.This methodology includes the first step of extraction of the location distribution and the second step of the extraction of internal environmental factors.The fishery ranching index(FRI1,FRI2)was established to extract the mussel and the large yellow croaker aquaculture area in Zhoushan,using Gaofen-1(GF-1)and Gaofen-6(GF-6)satellite data with a special resolution of 2 m.In the second step,the environmental factors such as sea surface temperature(SST),chlorophyll a(Chl-a)concentration,current and tide,suspended sediment concentration(SSC)in mussel aquaculture area and large yellow croaker aquaculture area were extracted and analyzed in detail.The results show the following three points.(1)For the extraction of the mussel aquaculture area,FRI1 and FRI2 are complementary,and the combination of FRI1 and FRI2 is suitable to extract the mussel aquaculture area.As for the large yellow croaker aquaculture area extraction,FRI2 is suitable.(2)Mussel aquaculture and the large yellow croaker aquaculture area in Zhoushan are mainly located on the side near the islands that are away from the eastern open waters.The water environment factor template suitable for mussel and large yellow croaker aquaculture was determined.(3)This two-step remote sensing method can be used for the preliminary screening of potential site selection for the mussels and large yellow croaker aquaculture area in the future.the fishery ranching index(FRI1,FRI2)in this paper can be applied to extract the mussel and large yellow croaker aquaculture areas in coastal waters around the world.

Prevalence of Malnutrition and Anaemia in Children ≤ 5 Years of Age in Some Conflict Hit Areas of Meme Division of the South West Region of Cameroon

Malnutrition refers to the deficiency, imbalances, or excesses in a person’s intake of energy or nutrients [1]. Khan defines anaemia as below level of Haemoglobin in red blood shown by a lower number of functioning red blood cells [2]. The crisis in the North West and South West Regions of Cameroon has led to several negative effects on children’s living conditions. There has been an increase in malnutrition and anaemia in the South West Region and Kumba in particular. The main objective of this study was “to examine the prevalence of malnutrition and anaemia in children ≤ 5 years of age in some conflict-hit areas of Meme Division”. A descriptive cross-sectional study was conducted in 2023 from March to June. We recruited 200 children ≤ 5 years into the study from three hospitals. The regional hospital annex in Kumba, Presbyterian General Hospital Kumba and the Ntam Hospital in Kumba. Socio-demographic factors were assessed using questionnaire, nutritional status was assessed by the use anthropometric measurements and an auto haematology analyser was used to determine anaemia. The overall prevalence of malnutrition in the study area was 40.5%. The prevalence of malnutrition varied significantly (P < 0.001) with the study sites. The overall prevalence of anaemia in the study area was 70.5%. The prevalence of anaemia was not significantly associated with the study sites. The prevalence of Malnutrition and Anaemia in children ≤ 5 years of age is very high in the Kumba municipalities. This could be attributed to the ongoing crisis which has caused a lot of social migrations from rural areas to Urban areas which are safer.

Better use of experience from other reservoirs for accurate production forecasting by learn-to-learn method

To assess whether a development strategy will be profitable enough,production forecasting is a crucial and difficult step in the process.The development history of other reservoirs in the same class tends to be studied to make predictions accurate.However,the permeability field,well patterns,and development regime must all be similar for two reservoirs to be considered in the same class.This results in very few available experiences from other reservoirs even though there is a lot of historical information on numerous reservoirs because it is difficult to find such similar reservoirs.This paper proposes a learn-to-learn method,which can better utilize a vast amount of historical data from various reservoirs.Intuitively,the proposed method first learns how to learn samples before directly learning rules in samples.Technically,by utilizing gradients from networks with independent parameters and copied structure in each class of reservoirs,the proposed network obtains the optimal shared initial parameters which are regarded as transferable information across different classes.Based on that,the network is able to predict future production indices for the target reservoir by only training with very limited samples collected from reservoirs in the same class.Two cases further demonstrate its superiority in accuracy to other widely-used network methods.

Comparison among the UECM Model, and the Composite Model in Forecasting Malaysian Imports

For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting.

Exploring well-being disparities between urban and rural areas:A case study in the Stavropol Territory,Russia

Rural areas are crucial for a country’s sustainable economy.New strategies are needed to develop rural areas to improve the well-being of rural population and generate new job opportunities.This is especially important in countries where agricultural production accounts for a significant share of the gross product,such as Russia.In this study,we identified the key indicators of satisfaction and differences between rural and urban citizens based on their social,economic,and environmental backgrounds,and determined whether there are well-being disparities between rural and urban areas in the Stavropol Territory,Russia.We collected primary data through a survey based on the European Social Survey framework to investigate the potential differences between rural and urban areas.By computing the regional well-being index using principal component analysis,we found that there was no statistically significant difference in well-being between rural and urban areas.Results of key indicators showed that rural residents felt psychologically more comfortable and safer,assessed their family relationships better,and adhered more to traditions and customs.However,urban residents showed better economic and social conditions(e.g.,infrastructures,medical care,education,and Internet access).The results of this study imply that we can better understand the local needs,advantages,and unique qualities,thereby gaining insight into the effectiveness of government programs.Policy-makers and local authorities can consider targeted interventions based on the findings of this study and strive to enhance the well-being of both urban and rural residents.

Glacier area change (1993-2019) and its relationship to debris cover, proglacial lakes, and morphological parameters in the Chandra-Bhaga Basin, Western Himalaya, India

Glacier inventories serve as critical baseline data for understanding the impacts of climate change on glaciers.The present study maps the outlines of glaciers in the Chandra-Bhaga Basin(western Himalaya)for the years 1993,2000,2010,and 2019 using Landsat Thematic Mapper(TM),Enhanced Thematic Mapper(ETM),and Operational Land Imager(OLI)datasets.A total of 251 glaciers,each having an area above 0.5 km^(2),were identified,which include 216 clean-ice and 35 debris-covered glaciers.Area changes are estimated for three periods:1993-2000,2000-2010,and 2010-2019.The total glacierized area was 996±62 km^(2) in 1993,which decreased to 973±70 km^(2) in 2019.The mean rate of glacier area loss was higher in the recent decade(2010-2019),at 0.036 km^(2),compared to previous decades(0.029 km^(2) in 2000-2010 and 0.025 km^(2) in 1993-2000).Supraglacial debris cover changes are also mapped over the period of 1993 and 2019.It is found that the supraglacial debris cover increased by 14.12±2.54 km^(2)(15.2%)during 1993-2019.Extensive field surveys on Chhota Shigri,Panchi II,Patsio,Hamtah,Mulkila,and Yoche Lungpa glaciers were carried out to validate the glacier outlines and supraglacial debris cover estimated using satellite datasets.Controls of various morphological parameters on retreat were also analyzed.It is observed that small,clean ice,south oriented glaciers,and glaciers with proglacial lakes are losing area at faster rates than other glaciers in the basin.

Petrogenesis, oxidation state and volatile content of Dongga tonalite in the Gangdese belt, Xizang: Implication for porphyry Cu mineralization

The Gangdese belt in Xizang has experienced both Jurassic subduction and Cenozoic continental collision processes, making it a globally renowned region for magmatic rocks and porphyry copper deposits. Numerous Jurassic intrusions have been identified in the belt. Apart from the quartz diorite porphyry in the large Xietongmen deposit, the Cu mineralization potential of other Jurassic intrusions in this belt remains unclear. This study presents zircon U–Pb dating and trace elements, apatite major and trace elements as well as published whole-rock geochemical and isotopic data of the Dongga tonalite in the central part of the Gangdese belt, aiming to reveal the petrogenesis, oxidation state, volatile content, and Cu mineralization potential of this intrusion. The Dongga tonalite has a zircon U–Pb age of 179.4 ± 0.9 Ma. It exhibits high whole-rock V/Sc values(8.76–14.6), relatively low apatite CeN/CeN*ratios(1.04–1.28), elevated zircon(Eu/Eu*)Nvalues(an average of 0.44), high Ce4+/Ce3+values(205–1896), and high ?FMQ values(1.3–3.7), collectively suggesting a high magmatic oxygen fugacity. The Dongga tonalite features amphibole phenocrysts, relatively high whole-rock Sr/Y ratios(20.3–58.9), and lower zircon Ti temperatures (502–740 ℃), reflecting a high magmatic water content. Estimation of magmatic sulfur content(0.002–0.024 wt%) based on apatite SO3contents indicates an enriched magma sulfur content. Combined with previous studies and the collected Sr–Nd–Hf isotopes, the Dongga tonalite is derived from juvenile lower crust related with subduction of the Neo-Tethys oceanic slab. When compared with Xietongmen orebearing porphyries, the Dongga tonalite exhibits remarkable similarities with the Xietongmen ore-bearing porphyries in terms of magma source, tectonic background, magmatic redox state, and volatile components, which indicates that the Dongga tonalite has a high porphyry Cu mineralization potential, and therefore, provides important guidance for the future mineralization exploration.

Weather-Driven Solar Power Forecasting Using D-Informer:Enhancing Predictions with Climate Variables

Precise forecasting of solar power is crucial for the development of sustainable energy systems.Contemporary forecasting approaches often fail to adequately consider the crucial role of weather factors in photovoltaic(PV)power generation and encounter issues such as gradient explosion or disappearance when dealing with extensive time-series data.To overcome these challenges,this research presents a cutting-edge,multi-stage forecasting method called D-Informer.This method skillfully merges the differential transformation algorithm with the Informer model,leveraging a detailed array of meteorological variables and historical PV power generation records.The D-Informer model exhibits remarkable superiority over competing models across multiple performance metrics,achieving on average a 67.64%reduction in mean squared error(MSE),a 49.58%decrease in mean absolute error(MAE),and a 43.43%reduction in root mean square error(RMSE).Moreover,it attained an R2 value as high as 0.9917 during the winter season,highlighting its precision and dependability.This significant advancement can be primarily attributed to the incorporation of a multi-head self-attention mechanism,which greatly enhances the model’s ability to identify complex interactions among diverse input variables,and the inclusion of weather variables,enriching the model’s input data and strengthening its predictive accuracy in time series analysis.Additionally,the experimental results confirm the effectiveness of the proposed approach.

Morphometry and Mineral Content in the Seeds and Soil of Two Species of Argemone L.(Papaveraceae)in the Central Part of the Chihuahuan Desert

The genus Argemone L.(Papaveraceae)is found widely distributed in Mexico’s Chihuahuan Desert(CD).Some species of this genus are of phytochemical or ethnobotanical interest.They are inedible plants considered as scrubs.To date they have not been broadly studied;thus,their ecology is,to our knowledge,unknown.The present work was centered around carrying out a morphometric analysis and the determination of minerals in the soil and seeds of the wild populations of Argemone at sites belonging to two ecoregions of the CD in Mexico.In April 2021 and April 2022,seeds of Argemone spp.,and soil samples were collected at 10 sites of the CD.The seeds were selected under a randomized design,and weight,length,diameter,thickness,buoyancy,and mineral content were determined.The soil samples were obtained under the Mexican regulation NOM-021-RECNAT-2000,and determinations of mineral content,electrical conductivity,apparent density,and soluble anions were performed.The information obtained was grouped by variable,species,and place of precedence.The statistical tests consisted of an ANOVA,Tukey means tests considering p≤0.05,and a Principal Components Analysis.Argemone pleiacantha exhibited differences in terms of weight(F=54.79,p=0.001),length(F=90.83,p=0.001),thickness(F=104.89,p=0.001),and diameter(F=155.82,p=0.001),and the differences in Argemone mexicana were in weight(F=46.71,p=0.001),thickness(F=187.49,p=0.001),length(F=191.56,p=0.001),and diameter(F=215.83,p=0.001).The evaluated seeds reached their maximal imbibition velocity at 24 h of evaluation.The content of the micro-and macro-nutrients analyzed in the seeds and soil suggest a tight relation with the morphometric characteristics of the seeds.

Geographical Accessibility to Basic Infrastructures and Services in Rural Senegal: The Case of the Niakhar Area

Access to basic infrastructure and services is a factor in economic development and an important aspect in combatting social and spatial disparities. But this access is often subject to several constraints, including geographical accessibility. In this article, we aim to analyze the geographical accessibility to basic infrastructure and services in the Niakhar area, using the improved two step floating catchment area method and local spatial association indicators. The results reveal that the areas with high accessibility to health and education infrastructures and services are mainly located along the south-east and north-west gradient, while those with low accessibility are found in the south-west and north-east center. They also show high accessibility to trade services in the center of the study area.

Food Hygiene and Safety Practices amongst Food Vendors in the Western Area, Sierra Leone

Food safety and hygiene practices require a multisectoral approach including food, water, sanitation, waste management, transport, education, trade, policies and programs that enable safe food preparation, storage and service. Unsafe food can cause illness keeping people from achieving their full potential and death. This was a descriptive study that uses a mixed method approach to derive insights into the characteristics of food vendors related to demography, knowledge, practices, infrastructure, compliance and recommendation for a policymaking framework. Using the Lemeshows’ sample size formula, 473 vendors from formal (restaurants) and informal (cookri-baffa/table top) sites were interviewed and observed. We found from discussions that respondents had a good understanding on how to keep food safe. However, observed practices were poor 93% handled food with their bare hands, 83% did not cover their hair, and 76% did not wear an apron whilst handling, preparing or serving food, 61% did not keep their finger nails clean or short and 57% did not wash their hand before preparing or serving food. Over half (51%) had access to a toilet but 32% reported their use required payment and emphasized their poor condition/inadequate management. Nearly half (47%) of the vending sites did not have a handwashing facility, with soap and water available. Only 7% reported having any authority oversight of food safety. Food safety and hygiene practices in most cookri shops and restaurants was ‘poor’ with very limited surveillance system in place by competent authorities for compliance of food operators. Hand washing, clean surroundings, and covered food were the most common and emphasized practices to mitigate the risks associated with unsafe food.