[Geochemical Survey Method of Land Quality in Land Parcel Scale City: A Case Study of the Initial Area of the Xiong'an New District]

In order to evaluate the land quality geochemical survey achievement in the service of the accurate management of urban land resources, the initial area of the Xiong'an New District as urbanization pathfinder in China is chosen as the research subject. The sample points were set by differential classification, and the spatial interpolation accuracy of the soil elements at a plot scale and a quantitative assessment of the consistency of the land plot (pattern spot) prediction evaluation were studied under the conditions of different sampling densities. The regional geochemical variation values randomly distributed on the plane can be reflected quantitatively by differential classification sampling, which can meet the basic demand of the quality attribute of a single plot (map spot) by the accurate management of urban land resources. The spatial variability of soil elements is mostly middle to moderate, and Cd, Cu, Pb, Hg, Se, N, P, and other elements of high spatial variability are affected by human industrial and agricultural production activities. Under the same sampling density, the larger the element variation coefficient, the worse the spatial interpolation accuracy. Although the interpolation accuracy of the same element index is affected by the sampling density, the increase in the sampling density could not identify the continuous component on the structure of the soil element content. The soil environment is clean, and the heavy metal content is lower than the GB15618-2018 standard. The interpolation results are basically consistent with the grading results of the measured values, while the contents of N, P, and K of the nutrient indices vary greatly, and the predicted and measured geochemical grades of the plots (map spot) differ substantially under the influence of factors such as human disturbance and spatial variability. The quantitative evaluation of the six different sampling densities indicates that the 16 points·km^-2 sampling density adopted in the geochemical survey and evaluation of urban land quality can satisfy the needs of an accurate control of urban land resources in the study area and similar areas. The research can provide key technologies to support and serve the accurate management of urban land resources for geochemical surveys and the evaluation of land quality in land parcel scale cities.

The associations between environmental quality and preterm birth in the United States, 2000-2005: a cross-sectional analysis

BACKGROUND

Many environmental factors have been independently associated with preterm birth (PTB). However, exposure is not isolated to a single environmental factor, but rather to many positive and negative factors that co-occur. The environmental quality index (EQI), a measure of cumulative environmental exposure across all US counties from 2000-2005, was used to investigate associations between ambient environment and PTB.

METHODS

With 2000-2005 birth data from the National Center for Health Statistics for the United States (n = 24,483,348), we estimated the association between increasing quintiles of the EQI and county-level and individual-level PTB; we also considered environmental domain-specific (air, water, land, sociodemographic and built environment) and urban-rural stratifications.

RESULTS

Effect estimates for the relationship between environmental quality and PTB varied by domain and by urban-rural strata but were consistent across county- and individual-level analyses. The county-level prevalence difference (PD (95% confidence interval) for the non-stratified EQI comparing the highest quintile (poorest environmental quality) to the lowest quintile (best environmental quality) was -0.0166 (-0.0198, -0.0134). The air and sociodemographic domains had the strongest associations with PTB; PDs were 0.0196 (0.0162, 0.0229) and -0.0262 (-0.0300, -0.0224) for the air and sociodemographic domain indices, respectively. Within the most urban strata, the PD for the sociodemographic domain index was 0.0256 (0.0205, 0.0307). Odds ratios (OR) for the individual-level analysis were congruent with PDs.

CONCLUSION

We observed both strong positive and negative associations between measures of broad environmental quality and preterm birth. Associations differed by rural-urban stratum and by the five environmental domains. Our study demonstrates the use of a large scale composite environment exposure metric with preterm birth, an important indicator of population health and shows potential for future research.

Construction of an environmental quality index for public health research

BACKGROUND

A more comprehensive estimate of environmental quality would improve our understanding of the relationship between environmental conditions and human health. An environmental quality index (EQI) for all counties in the U.S. was developed.

METHODS

The EQI was developed in four parts: domain identification; data source acquisition; variable construction; and data reduction. Five environmental domains (air, water, land, built and sociodemographic) were recognized. Within each domain, data sources were identified; each was temporally (years 2000-2005) and geographically (county) restricted. Variables were constructed for each domain and assessed for missingness, collinearity, and normality. Domain-specific data reduction was accomplished using principal components analysis (PCA), resulting in domain-specific indices. Domain-specific indices were then combined into an overall EQI using PCA. In each PCA procedure, the first principal component was retained. Both domain-specific indices and overall EQI were stratified by four rural-urban continuum codes (RUCC). Higher values for each index were set to correspond to areas with poorer environmental quality.

RESULTS

Concentrations of included variables differed across rural-urban strata, as did within-domain variable loadings, and domain index loadings for the EQI. In general, higher values of the air and sociodemographic indices were found in the more metropolitan areas and the most thinly populated areas have the lowest values of each of the domain indices. The less-urbanized counties (RUCC 3) demonstrated the greatest heterogeneity and range of EQI scores (-4.76, 3.57) while the thinly populated strata (RUCC 4) contained counties with the most positive scores (EQI score ranges from -5.86, 2.52).

CONCLUSION

The EQI holds promise for improving our characterization of the overall environment for public health. The EQI describes the non-residential ambient county-level conditions to which residents are exposed and domain-specific EQI loadings indicate which of the environmental domains account for the largest portion of the variability in the EQI environment. The EQI was constructed for all counties in the United States, incorporating a variety of data to provide a broad picture of environmental conditions. We undertook a reproducible approach that primarily utilized publically-available data sources.

Construction of an environmental quality index for public health research

BACKGROUND

A more comprehensive estimate of environmental quality would improve our understanding of the relationship between environmental conditions and human health. An environmental quality index (EQI) for all counties in the U.S. was developed.

METHODS

The EQI was developed in four parts: domain identification; data source acquisition; variable construction; and data reduction. Five environmental domains (air, water, land, built and sociodemographic) were recognized. Within each domain, data sources were identified; each was temporally (years 2000-2005) and geographically (county) restricted. Variables were constructed for each domain and assessed for missingness, collinearity, and normality. Domain-specific data reduction was accomplished using principal components analysis (PCA), resulting in domain-specific indices. Domain-specific indices were then combined into an overall EQI using PCA. In each PCA procedure, the first principal component was retained. Both domain-specific indices and overall EQI were stratified by four rural-urban continuum codes (RUCC). Higher values for each index were set to correspond to areas with poorer environmental quality.

RESULTS

Concentrations of included variables differed across rural-urban strata, as did within-domain variable loadings, and domain index loadings for the EQI. In general, higher values of the air and sociodemographic indices were found in the more metropolitan areas and the most thinly populated areas have the lowest values of each of the domain indices. The less-urbanized counties (RUCC 3) demonstrated the greatest heterogeneity and range of EQI scores (-4.76, 3.57) while the thinly populated strata (RUCC 4) contained counties with the most positive scores (EQI score ranges from -5.86, 2.52).

CONCLUSION

The EQI holds promise for improving our characterization of the overall environment for public health. The EQI describes the non-residential ambient county-level conditions to which residents are exposed and domain-specific EQI loadings indicate which of the environmental domains account for the largest portion of the variability in the EQI environment. The EQI was constructed for all counties in the United States, incorporating a variety of data to provide a broad picture of environmental conditions. We undertook a reproducible approach that primarily utilized publically-available data sources.