Correlation analysis of the urban heat island effect and the spatial and temporal distribution of atmospheric particulates using TM images in Beijing

Modeling spatio-temporal assessment of land use land cover of Lahore and its impact on land surface temperature using multi-spectral remote sensing data

Urban sprawl, also widely known as urbanization, is one of the significant problems in the world. This research aims to assess and predict the urban growth and impact on land surface temperature (LST) of Lahore as well as land use and land cover (LULC) with a cellular automata Markov chain (CA-Markov chain). LULC and LST distributions were mapped using Landsat (5, 7, and 8) data from 1990, 2004, and 2018. Long-term changes to the landscape were simulated using a CA-Markov model at 14-year intervals from 2018 to 2046. Results indicate that the built-up area was increased from 342.54 (18.41%) to 720.31 (38.71%) km². Meanwhile, barren land, water, and vegetation area was decreased from 728.63 (39.16%) to 544.83 (29.28%) km², from 64.85 (3.49%) to 34.78 (1.87%) km², and from 724.53 (38.94%) to 560.63 (30.13%) km², respectively. In addition, urban index, a non-vegetation index, accurately predicted LST, showing the maximum correlation R² = 0.87 with respect to retrieved LST. According to CA-Markov chain analysis, we can predict the growth of built-up area from 830.22 to 955.53 km² between 2032 and 2046, based on the development from 1990 to 2018. As urban index as the predictor anticipated that the LST 20-23 °C and 24-27 °C, regions would all decline in coverage from 5.30 to 4.79% and 15.79 to 13.77% in 2032 and 2046, while the temperature 36-39 °C regions would all grow in coverage from 15.60 to 17.21% of the city. Our results indicate severe conditions, and the authorities should consider some strategies to mitigate this problem. These findings are significant for the planning and development division to ensure the long-term usage of land resources for urbanization expansion projects in the future.

Spatio-temporal assessment of land use land cover based on trajectories and cellular automata Markov modelling and its impact on land surface temperature of Lahore district Pakistan

This research aims to assess the urban growth and impact on land surface temperature (LST) of Lahore, the second biggest city in Pakistan. In this research, various geographical information system (GIS) and remote sensing (RS) techniques (maximum likelihood classification (MLC)) LST, and different normalized satellite indices have been implemented to analyse the spatio-temporal trends of Lahore city; by using Landsat for 1990, 2004, and 2018. The development of integrated use of RS and GIS and combined cellular automata-Markov models has provided new means of assessing changes in land use and land cover and has enabled the projection of trajectories into the future. Results indicate that the built-up area and bare area increased from 15,541 (27%) to 23,024 km² (40%) and 5756 km² (10%) to 13,814 km² (24%). Meanwhile, water area and vegetation were decreased from 2302 km² (4%) to 1151 km² (2%) and 33,961 km² (59%) to 19,571 km² (34%) respectively. Under this urbanization, the LST of the city was also got affected. In 1990, the mean LST of most of the area was between 14 and 28 ℃, which rose to 22-28 ℃ in 2004 and 34 to 36 ℃ in 2018. Because of the shift of vegetation and built-up land, the surface reflectance and roughness of each land use land cover (LULC) class are different. The analysis established a direct correlation between Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI) with LST and an indirect correlation among Soil Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI), and Built-up Index (BI) with LST. The results are important for the planning and development department since they may be used to guarantee the sustainable utilization of land resources for future urbanization expansion projects.

Combining GOES-R and ECOSTRESS land surface temperature data to investigate diurnal variations of surface urban heat island

The surface urban heat island (SUHI) phenomenon is characterized by both high spatial and temporal variability, while its diurnal (i.e., diel) variations have rarely been investigated because traditional satellites and sensors flying on polar orbits (e.g., Landsat, MODIS) have no diurnal sampling capability. Here we combined land surface temperature (LST) data from the Geostationary Operational Environmental Satellites (GOES-R) and the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to explore the diurnal variations of SUHI and thermal differentiation among various land covers over the Boston Metropolitan Area. With the combined use of the LST data from GOES-R and ECOSTRESS, we took advantage of the strengths of both GOES-R (i.e., high frequency in each day and night) and ECOSTRESS (i.e., much finer spatial resolution). The SUHI intensity of the urban-core and suburban areas both exhibited clear diurnal patterns for different seasons: a continuous increase in the SUHI intensity from sunrise to noon and a decrease thereafter to sunset, followed by a relatively low and constant intensity during nighttime. The LST contrasts among different land cover types were clearly larger in the daytime than at nighttime and peaked around midday. At noon in summer, the LST of 'Developed, High Intensity' was 2.6 °C higher than that of 'Developed, Medium Intensity', and about 4.6 °C higher than that of "Developed, Open Space" and "Developed, Low Intensity". Controlling the percent impervious surface in construction land at a relatively low level (e.g., below ~49%) could effectively alleviate the impacts of SUHI. Compared with GOES-R data, ECOSTRESS LST is suitable for monitoring the diurnal variations of intracity thermal environment at the subdistrict (or neighborhood) scale. Our study highlights the value of the combined use of geostationary satellite and ECOSTRESS LST in exploring the diurnal cycling of the SUHI, and can help inform urban planning and land-based climate mitigation policies in the context of climate change.

Do industrial parks generate intra-heat island effects in cities? New evidence, quantitative methods, and contributing factors from a spatiotemporal analysis of top steel plants in China

Industrial parks emit large amounts of anthropogenic heat and aggravate the urban heat island effect, which has become a severe environmental problem worldwide. Few studies explored if the warming effect generated by concentrated industrial facilities (i.e., steel plants in this study) produces an intra-heat island effect in urban built-up areas. Sufficient evidence of an industrial heat island (IHI) effect is lacking, and new quantitative methods are urgently needed to address these issues. Therefore, we proposed a new scheme to quantify the warming effect of large, heat-emitting urban objects versus complex surroundings, and the IHI effect was accordingly defined at a finer scale. This study separated the industrial park from other artificial lands and comprehensively estimated the IHI effects' spatiotemporal variation. The IHI intensities were measured based on varied natural and urbanized references, which provided new evidence for the existence of the IHI effect over space and seasons. The land surface temperature (LST) profiles delineated the downward trend in LST variation from inside to surroundings in the IHI cases on both spatial and temporal scales. The time-series analysis revealed that the IHI effects demonstrated more significant disparities regarding the LSTs between the industrial parks and their surrounding backgrounds during warm seasons than in cold seasons. And a more severe IHI effect was observed in spring and summer, and the weakest IHI intensity occurred in winter. Moreover, the IHI intensity is positively associated to the anthropogenic heat, indicating that the industrial activities contribute to the increased LSTs of the industrial park to a great extent. The rationale of the IHI effect can broaden insight for understanding how urban industrial heat sources influence the regional thermal environment, especially at a finer scale.

Study of the Urban Heat Island (UHI) Using Remote Sensing Data/Techniques: A Systematic Review

Urban Heat Islands (UHI) consist of the occurrence of higher temperatures in urbanized areas when compared to rural areas. During the warmer seasons, this effect can lead to thermal discomfort, higher energy consumption, and aggravated pollution effects. The application of Remote Sensing (RS) data/techniques using thermal sensors onboard satellites, drones, or aircraft, allow for the estimation of Land Surface Temperature (LST). This article presents a systematic review of publications in Scopus and Web of Science (WOS) on UHI analysis using RS data/techniques and LST, from 2000 to 2020. The selection of articles considered keywords, title, abstract, and when deemed necessary, the full text. The process was conducted by two independent researchers and 579 articles, published in English, were selected. Qualitative and quantitative analyses were performed. Cfa climate areas are the most represented, as the Northern Hemisphere concentrates the most studied areas, especially in Asia (69.94%); Landsat products were the most applied to estimates LST (68.39%) and LULC (55.96%); ArcGIS (30.74%) was most used software for data treatment, and correlation (38.69%) was the most applied statistic technique. There is an increasing number of publications, especially from 2016, and the transversality of UHI studies corroborates the relevance of this topic.

Spatiotemporal Differentiation of Land Surface Thermal Landscape in Yangtze River Delta Region, China

Advancements in the integrated development of the Yangtze River Delta are changing the structure and function of the surface thermal landscape and triggering a series of ecological and environmental problems. Therefore, examining the spatiotemporal differentiation characteristics and evolution laws of this land surface thermal landscape has great theoretical and practical significance in the context of optimizing functional zoning and realizing the harmonious development of the economy, society and nature. The paper uses the LST (land surface temperature) data retrieved by MODIS (MOD11A2) remote sensing satellites in 2007, 2010, 2013, 2016 and 2019 to extract a land surface thermal rating map of the Yangtze River Delta region, and to analyze the spatiotemporal differentiation in the land surface thermal landscape, combining of the land surface thermal landscape strip profile and thermal landscape pattern indices. The results show that the LST in the Yangtze River Delta region has increased in the past 12 years, the proportion of middle-, sub-high- and high-temperature zones increased by 33.42%, and the high-temperature zone has gradually extended into inland areas. The high-temperature zones in the areas surrounding core cities such as Shanghai, Nanjing, and Hangzhou have expanded. The corridor effect of thermal changes on the surface is obvious. The degree of aggregation in the lower-temperature areas has gradually decreased. The degree of aggregation in the higher-temperature regions has increased. The patch types of thermal landscape pattern increase, and the distribution of landscape area among various types tends to be even. this trend is most significant in optimized development region.

Spatial and temporal analysis of urban heat island using Landsat satellite images

The average land surface temperature (LST) of Earth has increased since the late nineteenth century due to the warming of the Earth's atmosphere. Increased surface temperatures, especially in cities, are a significant environmental problem that intensifies urban heat islands (UHIs). In this study, land surface temperature, urban thermal field variance index (UTFVI), and UHI index were mapped using Landsat 4, 5, 7, and 8 satellite images to identify the distribution and determine the intensities of the UHI. Maps of land use at multi-year intervals between 1995 and 2016 were created using the support vector machine (SVM) method. These were used to compare LST variations to land-use changes and to determine the linkages between the two. The results showed that the highest recorded temperatures in Ahvaz, the capital of Khozestan Province, Iran, occurred in areas of bare land (42.93°C) and residential development (40.06°C) in 2017. Land use classification showed that the highest classification accuracy (in 2016) was 93%. The most varying extents of land use in Ahvaz were bare lands, residential lands, and green spaces. Green spaces in the study area in 1995 and 2016 covered 14% and 7% of the area, respectively, which showed a 50% reduction in green space over 21 years. A composite map of UTFVI and UHI showed that the locations classified as very hot had the worst UTFVI. The results of this study of Ahvaz, Iran's heat islands, can inform and guide urban planners in locational matters and in efforts to mitigate and adapt changing land uses in order to limit the intensification of the UHI.

Changes in protist communities in drainages across the Pearl River Delta under anthropogenic influence

Drainages in the Pearl River Delta urban agglomeration (PRDUA) host vital aquatic ecosystems and face enormous pressures from human activities in one of the largest urban agglomerations in the world. Despite being crucial components of aquatic ecosystems, the interactions and assembly processes of the protistan community are rarely explored in areas with serious anthropogenic disturbance. To elucidate the mechanisms of these processes, we used environmental DNA sequencing of 18S rDNA to investigate the influence of environmental factors and species interactions on the protistan community and its assembly in drainages of the PRDUA during summer. The protistan community showed a high level of diversity and a marked spatial pattern in this region. Community assembly was driven primarily by stochastic processes based on the Sloan neutral community model, explaining 74.28%, 75.82%, 73.67%, 74.40% and 51.24% of community variations in the BJ (Beijiang), XJ (Xijiang), PRD (Pearl River Delta), PRE (Pearl River Estuary) areas and in total, respectively. Meanwhile, environmental variables including temperature, pH, dissolved oxygen, transparency, nutrients and land use were strongly correlated with the composition and assembly of the protistan community, explaining 40.40% of variation in the protistan community. Furthermore, the bacterial community was simultaneously analysed by the 16S rDNA sequencing. Co-occurrence network analysis revealed that species interactions within bacteria (81.41% positive) or protists (82.80% positive), and those between bacteria and protists (50% positive and 50% negative) impacted the protistan community assembly. In summary, stochastic processes dominated, whereas species interactions and environmental factors also played important roles in shaping the protistan communities in drainages across the PRDUA. This study provides insights into the ecological patterns, assembly processes and species interactions underlying protistan dynamics in urban aquatic ecosystems experiencing serious anthropogenic disturbance.

Urban Vegetation in Air Quality Management: A Review and Policy Framework

Recent episodes of high air pollution concentration levels in many Polish cities indicate the urgent need for policy change and for the integration of various aspects of urban development into a common platform for local air quality management. In this article, the focus was placed on the prospects of improving urban air quality through proper design and protection of vegetation systems within local spatial planning strategies. Recent studies regarding the mitigation of air pollution by urban greenery due to deposition and aerodynamic effects were reviewed, with special attention given to the design guidelines resulting from these studies and their applicability in the process of urban planning. The conclusions drawn from the review were used to conduct three case studies: in Gdańsk, Warsaw, and Poznań, Poland. The existing local urban planning regulations for the management of urban greenery were critically evaluated in relation to the findings of the review. The results indicate that the current knowledge regarding the improvement of urban air quality by vegetation is not applied in the process of urban planning to a sufficient degree. Some recommendations for alternative provisions were discussed.

Remote Sensing-Based Quantification of the Relationships between Land Use Land Cover Changes and Surface Temperature over the Lower Himalayan Region

Population growth and population inflow from other regions has caused urbanization which altered land use land cover (LULC) in the lower Himalayan regions of Pakistan. This LULC change increased the land surface temperature (LST) in the region. LULC and LST changes were assessed for the period of 1990–2017 using Landsat data and the support vector machine (SVM) method. A combined cellular automata and artificial neural network (CA-ANN) prediction model was used for simulation of LULC changes for the period of 2032 and 2047 using transition potential matrix obtained from the data years of 2002 and 2017. The accuracy of the CA-ANN model was validated using simulated and classified images of 2017 with correctness value of 70% using validation modules in QGIS. The thermal bands of Landsat images from the years 1990, 2002 and 2017 were used for LST derivation. LST acquired for this period was then modeled for 2032 and 2047 using urban indices (UI) and linear regression analysis. The SVM land cover classification results showed a 5.75% and 4.22% increase in built-up area and bare soil respectively, while vegetation declined by 9.88% during 1990–2017. The results of LST for LULC classes showed that the built-up area had the highest mean LST as compared to other classes. The future projection of LULC and LST showed that the built-up area may increase by 12.48% and 14.65% in 2032 and 2047, respectively, of the total LULC area which was ~11% in 2017. Similarly, the area with temperature above 30 °C could be 44.01% and 58.02% in 2032 and 2047, respectively, of the total study area which was 18.64% in 2017. This study identified major challenges for urban planners to mitigate the urban heat island (UHI) phenomenon. In order to address the UHI in the study area, an urban planner might focus on urban plantation and decentralization of urban areas.

Field Study on the Microclimate of Public Spaces in Traditional Residential Areas in a Severe Cold Region of China

As residential environment science advances, the environmental quality of outdoor microclimates has aroused increasing attention of scholars majoring in urban climate and built environments. Taking the microclimate of a traditional residential area in a severe cold city as the study object, this study explored the influence of spatial geometry factors on the microclimate of streets and courtyards by field measurements, then compared the differences in microclimate of distinct public spaces. The results are as follows. (1) The temperature of a NE-SW (Northeast-Southwest) oriented street was higher than that of a NW-SE (Northwest-Southeast) oriented street in both summer and winter, with an average temperature difference of 0.7–1.4 °C. The wind speeds in the latter street were slower, and the difference in average wind speed was 0.2 m/s. (2) In the street with a higher green coverage ratio, the temperature was much lower, a difference that was more obvious in summer. The difference in mean temperature was up to 1.2 °C. The difference in wind speed between the two streets was not obvious in winter, whereas the wind speed in summer was significantly lower for the street with a higher green coverage ratio, and the difference in average wind speed was 0.7 m/s. (3) The courtyards with higher SVF (sky view factor) had higher wind speeds in winter and summer, and the courtyards with larger SVF values had higher temperatures in summer, with an average temperature difference of 0.4 °C. (4) When the spaces had the same SVF values and green coverage ratios, the temperature of the street and courtyard were very similar, in both winter and summer. The wind speed of the street was significantly higher than the courtyard in summer, and the wind speed difference was 0.4 m/s.

Spatiotemporal patterns and characteristics of remotely sensed region heat islands during the rapid urbanization (1995-2015) of Southern China

Urban agglomeration has become the most salient feature of global urbanization in recent decades, while spatiotemporal patterns and evolution remain poorly understood in urban agglomerations, which limit the decision-makers to make more informed decisions to improve the regional environment. Here we selected one of the most rapidly urbanized regions in the world - Pearl River Delta Metropolitan Region (PRDR), located in southern China, as the case. Landsat images spanning from 1995 to 2015 were used to retrieve land surface temperature (LST). Four types of regional heat island (RHI) degree were defined for further analysis. Then multi-scale spatiotemporal patterns and characteristics of RHI were identified with the help of cloud-based computing, spatial and landscape analysis. We found that (1) traditional urban heat island (UHI) appears as an RHI on an urban agglomeration scale. In PRDR, we found RHI expended with increasing connectivity, especially in the estuary areas where isolated UHI gradually merged during the rapid urbanization. (2) The contribution of main cities in PRDR to RHI and the evolutionary trends and pattern, which is changed from a west-east to a southwest-northeast gradient, have been revealed. (3) Considering the scale effect and different RHI categories, we revealed that during the urbanization, the aggregation of the RHI is significant on a larger-scale, and the area of 4 °C ≤ Relative LST ≤ 8 °C is the stable and high-risk area, which provide scientific bases for the governance of the thermal environment on the regional scale. (4) The study also indicates the cooling effect of forests and water is better than that of grassland, while the cooling effect of grassland is uncertain. The methods and results of this study not only have implications on environmental planning and management in the PRDR but also provide useful insights into the thermal environment research and practice in other urban agglomerations.

Interaction between urban heat island and urban pollution island during summer in Berlin

Urban Heat Island (UHI) and Urban Pollution Island (UPI) are two major problems of the urban environment and have become more serious with rapid urbanization. Since UHI and UPI can interact with each other, these two issues should be studied concurrently for a better urban environment. This study investigated the interaction between the UHI and UPI in Berlin, through a combined analysis of in-situ and remote sensing observations of aerosols and meteorological variables in June, July, and August from 2010 to 2017. The atmospheric UHI (AUHI), surface UHI (SUHI), atmospheric UPI (AUPI), and near-surface UPI (NSUPI) were analyzed. The SUHI and AUPI are represented by the remote sensing land surface temperature (LST) and aerosol optical depth (AOD), and the AUHI and NSUPI are represented by the in-situ air temperature and Particulate Matter (PM10) concentrations. The study area shows spatial consistency between SUHI and AUPI, with higher LST and AOD in the urban areas. UHI strengthens the turbulent dispersion of particles in the urban areas, decreasing the NSUPI. The NSUPI intensity shows a negative relationship with the AUHI intensity, especially at night with a correlation coefficient of -0.31. The increased aerosols in urban atmosphere reduce the incoming solar radiation and increase the atmospheric longwave radiation in the urban areas. The response of the surface to the change of absorbed radiation is strong at night and weak during the day. This study estimates that the SUHI intensity is enhanced by around 12% at clear night by the increased absorbed radiation in the urban areas using an attribution method. The goal of this paper is to strengthen the understanding of the interactive influence between UHI and UPI and provide a basis for designing mitigation strategies of UHI and UPI.

External costs of PM2.5 pollution in Beijing, China: Uncertainty analysis of multiple health impacts and costs

Some cities in China are facing serious air pollution problems including high concentrations of particles, SO₂ and NO_x. Exposure to PM2.5, one of the primary air pollutants in many cities in China, is highly correlated with various adverse health impacts and ultimately represents a cost for society. The aim of this study is to assess health impacts and external costs related to PM2.5 pollution in Beijing, China with different baseline concentrations and valuation methods. The idea is to provide a reasonable estimate of the total health impacts and external cost due to PM2.5 pollution, as well as a quantification of the relevant uncertainty. PM2.5 concentrations were retrieved for the entire 2012 period in 16 districts of Beijing. The various PM2.5 related health impacts were identified and classified to avoid double counting. Exposure-response coefficients were then obtained from literature. Both the value of statistical life (VSL) and the amended human capital (AHC) approach were applied for external costs estimation, which could provide the upper and lower bound of the external costs due to PM2.5. To fully understand the uncertainty levels, the external cost distribution was determined via Monte Carlo simulation based on the uncertainty of the parameters such as PM2.5 concentration, exposure-response coefficients, and economic cost per case. The results showed that the external costs were equivalent to around 0.3% (AHC, China's guideline: C₀ = 35 μg/m³) to 0.9% (VSL, WHO guideline: C₀ = 10 μg/m³) of regional GDP depending on the valuation method and on the assumed baseline PM2.5 concentration (C₀). Among all the health impacts, the economic loss due to premature deaths accounted for more than 80% of the overall external costs. The results of this study could help policymakers prioritizing the PM2.5 pollution control interventions and internalize the external costs through the application of economic policy instruments.

Monetary Valuation of PM10-Related Health Risks in Beijing China: The Necessity for PM10 Pollution Indemnity

Severe health risks caused by PM₁₀ (particulate matter with an aerodynamic diameter ≤10 μm) pollution have induced inevitable economic losses and have rendered pressure on the sustainable development of society as a whole. In China, with the “Polluters Pay Principle”, polluters should pay for the pollution they have caused, but how much they should pay remains an intractable problem for policy makers. This paper integrated an epidemiological exposure-response model with economics methods, including the Amended Human Capital (AHC) approach and the Cost of Illness (COI) method, to value the economic loss of PM₁₀-related health risks in 16 districts and also 4 functional zones in Beijing from 2008 to 2012. The results show that from 2008 to 2012 the estimated annual deaths caused by PM₁₀ in Beijing are around 56,000, 58,000, 63,000, 61,000 and 59,000, respectively, while the economic losses related to health damage increased from around 23 to 31 billion dollars that PM₁₀ polluters should pay for pollution victims between 2008 and 2012. It is illustrated that not only PM₁₀ concentration but also many other social economic factors influence PM₁₀-related health economic losses, which makes health economic losses show a time lag discrepancy compared with the decline of PM₁₀ concentration. In conclusion, health economic loss evaluation is imperative in the pollution indemnity system establishment and should be considered for the urban planning and policy making to control the burgeoning PM₁₀ health economic loss.

Health risk assessment of inhalable particulate matter in Beijing based on the thermal environment

Inhalable particulate matter (PM₁₀) is a primary air pollutant closely related to public health, and an especially serious problem in urban areas. The urban heat island (UHI) effect has made the urban PM₁₀ pollution situation more complex and severe. In this study, we established a health risk assessment system utilizing an epidemiological method taking the thermal environment effects into consideration. We utilized a remote sensing method to retrieve the PM₁₀ concentration, UHI, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI). With the correlation between difference vegetation index (DVI) and PM₁₀ concentration, we utilized the established model between PM₁₀ and thermal environmental indicators to evaluate the PM₁₀ health risks based on the epidemiological study. Additionally, with the regulation of UHI, NDVI and NDWI, we aimed at regulating the PM₁₀ health risks and thermal environment simultaneously. This study attempted to accomplish concurrent thermal environment regulation and elimination of PM₁₀ health risks through control of UHI intensity. The results indicate that urban Beijing has a higher PM₁₀ health risk than rural areas; PM₁₀ health risk based on the thermal environment is 1.145, which is similar to the health risk calculated (1.144) from the PM₁₀ concentration inversion; according to the regulation results, regulation of UHI and NDVI is effective and helpful for mitigation of PM₁₀ health risk in functional zones.

Socio-economic factors of bacillary dysentery based on spatial correlation analysis in Guangxi Province, China

BACKGROUND

In the past decade, bacillary dysentery was still a big public health problem in China, especially in Guangxi Province, where thousands of severe diarrhea cases occur every year.

METHODS

Reported bacillary dysentery cases in Guangxi Province were obtained from local Centers for Diseases Prevention and Control. The 14 socio-economic indexes were selected as potential explanatory variables for the study. The spatial correlation analysis was used to explore the associations between the selected factors and bacillary dysentery incidence at county level, which was based on the software of ArcGIS10.2 and GeoDA 0.9.5i.

RESULTS

The proportion of primary industry, the proportion of younger than 5-year-old children in total population, the number of hospitals per thousand persons and the rates of bacillary dysentery incidence show statistically significant positive correlation. But the proportion of secondary industry, per capital GDP, per capital government revenue, rural population proportion, popularization rate of tap water in rural area, access rate to the sanitation toilets in rural, number of beds in hospitals per thousand persons, medical and technical personnel per thousand persons and the rate of bacillary dysentery incidence show statistically significant negative correlation. The socio-economic factors can be divided into four aspects, including economic development, health development, medical development and human own condition. The four aspects were not isolated from each other, but interacted with each other.