Source Apportionment and Source-specific Health Risk of Heavy Metals in Urban Road Dust of Jinan City, East China

For a better regional and source-risk-based control of heavy metals in urban environments, this study provides a source-specific health risk assessment by combining the models of United States Environmental Protection Agency health risk assessment and positive matrix factorization (PMF). The calculated data were optimized by the geochemical speciation of target 10 potentially toxic heavy metals. The results demonstrated that the mean concentrations of most heavy metals in urban dust of Jinan City exceeded their corresponding background values, especially that of cadmium (Cd) and zinc (Zn) exhibiting a mean of 12.9 and 7.84 times those of their backgrounds. Cd, Zn, copper, lead and manganese in road dust existed mainly in extractable forms, exhibiting higher bio-availability. The PMF receptor model determined four sources of heavy metals in urban road dust, namely industrial discharges (41.1%), natural and coal combustion sources (27.8%), traffic emissions (22.8%), and building material and manufacturing sources (8.3%). All the studied heavy metals presented low or negligible non-carcinogenic risk (non-CR) for adults and children, while the lifetime carcinogenic risk (CR) of Cd was in an acceptable level. Regarding source-specific health risks, the highest non-CR was derived from industrial discharges, while CR from traffic emissions, which were mainly associated with the higher content and bio-availability of Pb and Cd in the dust. Moreover, the risk contributions of industrial discharges and traffic emissions were 35.9% and 60.6% for non-CR and CR, respectively, presenting a significant difference with the apportioned source characteristics, thus deep-revealing the potentially source-based risks of heavy metal in urban environment.

Functional Area Recognition and Use-Intensity Analysis Based on Multi-Source Data: A Case Study of Jinan, China

This paper proposes a GIS-based field model for hot-spot extraction based on POI data and analyzes the use intensity of functional areas by using Tencent location data to identify and describe the morphological characteristics and dynamic use intensity of facilities in urban functional areas. Taking the four districts of Jinan City Center as an example, we used the generalized symmetric structure spectrum and digital field-based hierarchical geo-information Tupu to extract facility hot spots. Tencent location data were then applied to quantify differences in the use intensity of functional areas between workday and weekend, as well as between daytime and nighttime. Finally, refined research on functional areas was realized from a dynamic point of view. Results showed that (1) the generalized symmetric structure spectrum and digital field-based hierarchical geo-information Tupu can identify and express the characteristics of the spatial distribution and hierarchical structures of urban facility hot spots at the horizontal and vertical levels, respectively; (2) overall, the distribution of all types of functional areas presents the characteristics of “circular structures,” which form a spatial pattern of “multi-center” groups and “single/mixed” functional areas; (3) aside from residential facilities, green space and square land facilities have the highest use intensity; this finding highlights the tourism characteristics of Jinan. Low-use intensity areas are distributed at the periphery of the four districts, while high-use intensity areas, the functional type of which is mainly business facilities, are mainly distributed around the urban area. These results are helpful to the development strategy of the city’s efforts to adapt to economic change and provide a scientific basis for the functional orientation of Jinan City.

Impact of Urban Expansion on Rain Island Effect in Jinan City, North China

Rapid urbanization leads to changes in urban micro meteorology, such as the urban heat island effect and rain island effect, which eventually brings about urban waterlogging and other problems. In this study, the data of precipitation, temperatures and impervious surfaces with long series and high resolution are used to study the rain island effect in Jinan City, China. MK-Sen’s slope estimator, Pettitt test and Pearson correlation analysis are used to quantitatively analyze the effects of urban expansion on extreme climate indices. The results show that Jinan City has experienced rapid urbanization since the 1978 economic reform, and the impervious surface areas have increased from 311.68 km2 (3.04%) in 1978 to 2389.50 km2 (23.33%) in 2017. Urban expansion has a significant impact on temperature, with large variations in extreme temperature indices over the intensive construction area relative to the sparse construction area, as well as significant positive correlations with impervious surfaces. Jinan City shows a certain degree of rain island effect, which seems to be spatially correlated with the urban heat island effect. The frequency of short-duration precipitation events significantly increases and the intensity of precipitation events generally increases. The magnitude and frequency of extreme precipitation indices in the intensive construction area significantly increases when compared to that in the sparse construction area, and they have a significant correlation with impervious surfaces. The tendency of Jinan City’s precipitation regime center shifts toward the intensive construction area.

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

In this study, a comprehensive assessment on precipitation estimation from the latest Version 06 release of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) algorithm is conducted by using 24 rain gauge observations at daily scale from 2001 to 2016. The IMERG V06 dataset fuses Tropical Rainfall Measuring Mission (TRMM) satellite data (2000–2015) and Global Precipitation Measurement (GPM) satellite data (2014–present), enabling the use of IMERG data for long-term study. Correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of satellite-derived precipitation estimation and measure the correspondence between satellite-derived and observed occurrence of precipitation events. The probability density distributions of precipitation intensity and influence of elevation on precipitation estimation were also examined. Results showed that, with high CC and low RMSE and RB, the IMERG Final Run product (IMERG-F) performs better than two other IMERG products at daily, monthly, and yearly scales. At daily scale, the ability of satellite products to detect general precipitation is clearly superior to the ability to detect heavy and extreme precipitation. In addition, CC and RMSE of IMERG products are high in Southeastern Jinan City, while RMSE is relatively low in Southwestern Jinan City. Considering the fact that the IMERG estimation of extreme precipitation indices showed an acceptable level of accuracy, IMERG products can be used to derive extreme precipitation indices in areas without gauged data. At all elevations, IMERG-F exhibits a better performance than the other two IMERG products. However, POD and FAR decrease and CSI increase with the increase of elevation, indicating the need for improvement. This study will provide valuable information for the application of IMERG products at the scale of a large city.