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ABSTRACTPhenological changes caused by urbanization may provide evidence of how vegetation responds to global warming. However, the phenological characteristics of vegetation in metropolitan areas have been poorly studied, especially in terms of spatiotemporal variations. In this study, we explored the applicability of Local Climate Zones (LCZs) to investigate the spatiotemporal variations in urban vegetation phenology by linking MODIS-derived phenology metrics with LCZs in the Austin metropolitan area in Texas, USA. We extracted three vegetation phenology metrics from MODIS data between 2008 and 2018, including the start of growing season, end of growing season, and length of the growing season (i.e. SOS, EOS, and LOS, respectively). The results showed that during the study period, the EOS and SOS gradually advanced, while LOS showed no obvious change. Statistical analysis was conducted to examine the spatiotemporal variations of the phenology metrics among different LCZs and along “Urban-Rural Gradients” (URGs). There were 37.5%, 75.0%, and 74.3% pairs of LCZs, indicating statistically significant phenological differences in terms of SOS, EOS, and LOS in 2012, respectively. In contrast, most pairs of URGs showed almost no differences in phenological metrics, especially in EOS. Geographically, SOS showed a fluctuating change with an advancing tendency, whereas the EOS decreased very slowly with distance from the city center (i.e. along the URGs). LCZs can be used to help identify distinctive phenology metrics with statistically significant differences, especially in EOS and LOS. Compared to URGs, LCZs offer a unique analytical framework for studying urban ecosystem patterns, functions, and dynamics. Lastly, LCZs can enable the identification of sensitive areas for ecological protection in support of sustainable urban development and environmental stewardship.

During the decomposition process of soil organic carbon (SOC), microbial products such as microbial necromass and microbial metabolites may form an important stable carbon (C) pool, called microbially derived C, which has different decomposition patterns from plant-derived C. However, current Earth System Models do not simulate this microbially derived C pool separately. Here, we incorporated the microbial necromass pool to the first-order kinetic model and the Michaelis–Menten model, respectively, and validated model behaviors against previous observation data from the decomposition experiments of 13C-labeled necromass. Our models showed better performance than existing models and the Michaelis–Menten model was better than the first-order kinetic model. Microbial necromass C was estimated to be 10–27% of total SOC in the study soils by our models and therefore should not be ignored. This study provides a novel modification to process-based models for better simulation of soil organic C under the context of global changes.

The discharge of acid mine drainage from abandoned high sulfur (S) coal mines has caused serious pollution in the Shandi River, Yangquan, Shanxi Province. To determine the impact of long-term acid mine drainage on the microorganisms in the river, we collected river sediments from a polluted tributary (Group P) and the mainstream of Shandi River (Group R) to study the bacterial diversity and community composition. The results showed that the tributary was seriously polluted by acid drainage from abandoned coal mines, with the pH value of the sediment being < 2.5, resulting in the low bacterial richness and diversity of the tributary samples. Acidophillic Fe- and S-metabolizing bacteria, such as Metallibacterium, Acidiphilium, and Acidithiobacillus,were the dominant genera in Group P samples, while the Group R was dominated by the neutral anaerobic iron-reducing bacteria Geothrixand Geobacter. Results of principal co-ordinates analysis (PCoA) revealed that the bacterial communities are significantly different between groups P and R, and the significant different species were mainly attributed to phylum Proteobacteria, Actinobacteria, and Acidobacteria. The distribution of the microbial community is mainly influenced by pH, and the Fe and Cd concentrations. Metallicactrium, the dominant genus, is negatively correlated with pH (R2= − 0.95) and positively correlated with Fe (R2= 0.99), while Geothrixand Geobacter, are mainly affected by the heavy metals. This study determined the impact of river pollution caused by abandoned coal mine drainage, especially on the microbial diversity and community composition within the river sediment.

During the decomposition process of soil organic carbon (SOC), microbial products such as microbial necromass and microbial metabolites may form an important stable carbon (C) pool, called microbially derived C, which has different decomposition patterns from plant-derived C. However, current Earth System Models do not simulate this microbially derived C pool separately. Here, we incorporated the microbial necromass pool to the first-order kinetic model and the Michaelis–Menten model, respectively, and validated model behaviors against previous observation data from the decomposition experiments of 13C-labeled necromass. Our models showed better performance than existing models and the Michaelis–Menten model was better than the first-order kinetic model. Microbial necromass C was estimated to be 10–27% of total SOC in the study soils by our models and therefore should not be ignored. This study provides a novel modification to process-based models for better simulation of soil organic C under the context of global changes.

ABSTRACTUrban heat islands (UHIs) are a key topic in urban climate studies. However, systematic criteria for UHI comparisons were lacking prior to 2012, when the concept of Local Climate Zones (LCZs) was introduced. By relating remotely sensed land surface temperature (LST) to LCZs, we explored the applicability of LCZs in surface urban heat island (SUHI) investigations and compared the LST variation within and among LCZs for the Austin, San Antonio, and Dallas-Fort Worth metropolitan areas in Texas, United States. Landsat 8 images from one summer and one winter day in 2015 were used to obtain LSTs to measure and model SUHIs. LCZs that were characterized by different land covers had the greatest LST variations, and LCZs that were further characterized by various urban morphological properties (including building density and height of roughness) also showed significant LST differences. Moreover, LCZ 9 (sparsely built), LCZ 10 (heavy industry), LCZ D (low plants), LCZ E (bare rock or paved), and LCZ F (bare soil or sand) tended to contribute to contradictory heating/cooling effects in different metropolitan areas, primarily due to the spatial arrangement and geographic locations of LCZs. The close association between LCZs and LST demonstrates that LCZs are valuable for comparative analysis of the SUHI phenomenon between different cities and can be helpful for examination of the evolution of SUHIs over time. Our findings further suggest that understanding the spatial distribution of LCZs can benefit the development of mitigation strategies for SUHIs.

ABSTRACTLight Detection and Ranging (LiDAR) technology is gradually being adopted as the primary technique for surface structure derivation. There is a tendency for users of LiDAR data to want the highest density data possible, but such dense data sets increase overall costs including financial, processing, and storage without significant value added to the derived outputs, particularly in the case of digital elevation models (DEMs). In this study, we employed three sampling methods (systematic sampling, simple random sampling, and stratified random sampling) to select points from the original LiDAR data set that represented six different sparse densities. Using the sampled data sets, DEMs were generated and sensitivity analyses were conducted to explore whether an optimum LiDAR point sampling method and density could be identified. Despite the fact that significant differences in descriptive statistics were not observed for the three sampling strategies or point densities, there are evident differences among the derived DEMs, variograms, and viewsheds for very sparse samples (e.g., less than 2 percent of the original data set). In particular, the systematic and random sampling methods resulted in noticeable differences compared to the stratified random sampling approach. This research contributes to a better understanding of how LiDAR point sampling can be used to reduce data volume without compromising the final DEM quality.

Environmentally Extended Input–Output (EEIO) tables have become a powerful element in supporting information-based environmental and economic policies. National- and provincial-level IO tables are currently published by the National Bureau of Statistics of the People’s Republic of China according to well-defined conventions. However, county-level IO tables are not provided as a rule by official statistics organizations. This paper conducts an overview of compiling EEIO tables for environmental and resources accounting at the county level and then answers several questions: First, what kind of data should be prepared for the compilation of county-level EEIO tables? Second, how can we set up comprehensive EEIO tables at the county level? Third, regarding the survey methods and the indirect modeling, which one should be chosen to build EEIO tables at the county level? Finally, what policy questions could such a table answer? EEIO tables at the county level can be used to predict the economic impacts of environmental policies and to perform trend and scenario analysis.