Urbanization can drive economic growth, but it may harm the quality of the urban environment if improper actions are performed. Environmental issues resulting from urbanization can negatively impact the health of city dwellers. Therefore, studying air pollutants is crucial to urban development. In this study, we focus on Xiamen and examine the distribution patterns of urban air pollutants over an extended period of time. The goal is to enhance Xiamen’s air quality and bridge the research gap in long-term air quality studies specific to Xiamen. Based on monitoring data from 2014 to 2021 spanning 8 years, this study analyzed the trends in atmospheric particulate matter (PM: PM10, PM2.5) and their relationship with meteorological factors (such as wind speed: WS, temperature: T, dew point temperature: DPT, height of the cloud ceiling: HCC) and the concentrations of other pollutants (SO2, NO2, CO, and O3). The results indicated that (1) The high air quality in Xiamen with the lowest PM values observed during summer and the highest during winter; NO2 and SO2 concentrations showed similar trends to PM, while O3 and CO concentrations varied differently. (2) In general, the maximum daily PM concentration was observed in the evening and early morning, while the lowest value appeared at noon. The concentrations of PMs were positively correlated with other pollutants, while T, WS, HCC (cloud cover of more than 70%), and DPT were negatively correlated with PM concentrations. (3) There exists a relationship between concentrations of atmospheric particulate matter, atmospheric pollutants, and meteorological factors. The wind direction had varying effects on PM concentration, with PM2.5 and PM10 concentrations showing consistent trends and higher concentrations of PMs observed when winds blew from the west, southeast, and northeast. This study also provides a summary of strategies for addressing different air pollutant distribution characteristics. The purpose of this study is to analyze the distribution patterns of air pollutants in Xiamen and provide valuable insights for improving the city’s air quality.

This article uses the entropy weight method to calculate China’s economic high-quality development index and carbon finance development index from 2000 to 2020. Additionally, it conducts empirical examinations to scrutinize impacts of carbon finance regarding quality-focused development of China’s economy through dynamic spatial models. The findings reveal that the advancement of carbon finance substantively promotes the quality-focused economy advancement through enhancing the overall factor productivity, thereby establishing a mediating effect. Concurrently, the allocation of resources in green fiscal expenditure assumes a moderating effect, subsequently amplifying the promoting effect of carbon finance regarding quality-focused economy advancement. Consequently, it is recommended that a robust financial milieu be established, energy efficiency be enhanced, scientific and technological progress be bolstered, total factor productivity be further augmented, and the expenditure on green conservation be escalated. These measures will effectively stimulate the quality-focused development of China’s economy.

Chile is currently facing a major drought that has caused several problems, most of them concentrated in terms of the availability of water for both human consumption and irrigation for agriculture. Under such conditions, the main instrument the government has at hand to assign water for agricultural use is the Water Scarcity Decree (WSD), which, among other aspects, allows for the extraction of underground water. However, this practice requires an important investment from the agricultural producer, making it only affordable by relatively larger producers. Therefore, under the current climatic conditions and a generalized lack of water, larger agricultural producers are the ones who benefit the most from the establishment of a WSD and thus have the incentives to use their political power to pressure for its issuing. Whereas conventional wisdom suggest that this is indeed the case, there is no previous evidence trying to link the size of agricultural exploitations and the likelihood of the establishment of a WSD. In the paper we use the share of large exploitations at the municipality level, as a measure that can proxy for local political power, and the establishment, the number, and duration of WSD within any given year. Consistent with the hypothesis, our results show that areas dominated by larger producers/exploitations are more likely to be declared as water scarce, to have more decrees in a year, and to have them in place for longer periods of time, even after controlling for socioeconomic characteristics and climatic conditions, such as precipitations and water flow.

This paper presents an alternative method for monitoring groundwater levels and estimating specific yields of an unconfined aquifer under different seasonal conditions. The approach employs the Time-Lapse Electrical Resistivity Imaging (TL-ERI) method and machine learning-based time series clustering. A TL-ERI survey was conducted at ten sites (WS01-WS10 sites) throughout the dry and wet seasons, with five-time measurements collected for each site, in the Taichung-Nantou Basin along the Wu River, Central Taiwan. The obtained resistivity raw data was inverted and converted into normalized water content values using Archie’s law, followed by applying the Van Genuchten (VG) model for the Soil Water Characteristic Curve to estimate the Groundwater Level (GWL), and estimated the theoretical specific yield (Sy) by computing the difference between the saturated and residual water contents of the fitted VG model. In addition, the specific yield capacity (Sc), representing the nature of the storage capacity in the aquifer, was also calculated. The results showed that this approach was able to estimate those hydrogeological parameters. The spatial distribution of the GWL reveals that during the dry-wet seasons from February to July, there was a high GWL that extended from southeast to northwest. Conversely, during the wet-dry seasons from July to October, the high GWL shrank, which can be attributed to recharge variations from rainfall events. The determined spatial distribution of Sy and Sc fall within the range of 0.03–0.24 and 0.14–0.25, respectively. To quantitatively establish areas of similar groundwater level changes along with the VG model parameter variations during the study period, a Time series Clustering analysis (TSC) was performed by utilizing Hierarchical Agglomerative Clustering (HAC). The findings suggest that the WS03 site is a promising area for further investigation due to its highest Sc value with a slight change in groundwater levels during the dry and wet seasons. This study brings an advanced development of the geoelectrical method to estimate regional hydrogeological parameters in an area with limited available groundwater observation wells, in different seasonal conditions for groundwater management purposes.

Certification schemes, aimed at simultaneously promoting ecologically sustainable agriculture and improving livelihood, are being utilized at a global scale. Among such certification schemes, the Rainforest Alliance is known as one of the most widely used environmental certification programs throughout the world. Previous studies have compared the ecological impacts of certified and non-certified farmlands, or evaluated the economic outcomes of certification. However, few studies have assessed the long-term impacts of the certification scheme. This paper attempts to analyze the long-term outcomes of the Rainforest Alliance certification program through a case study of coffee farming practices in southwestern Ethiopia. We conducted in-depth qualitative interviews with key informants who were deeply involved in the certification program, together with field observations and secondary data collection. The results of the assessment indicated that some areas of the certified coffee forests have been deforested or ecologically degraded and that the Rainforest Alliance program requirements were not uniformly applied. The possible causes include rapid population increase, government policies promoting intensive coffee production, presence of members who did not participate in the certification program, a lack of conservation incentives, and loopholes in the auditing process. To determine the overall success of the Rainforest Alliance certification program would require: monitoring of population growth rates and providing alternative livelihood opportunities, promoting collaboration between environmental and agricultural government authorities, conducting a more stringent on-site inspection, and to provide direct incentives for environmental conservation to all farmers living in or near the certified areas.

Vegetation models for climate adaptation and mitigation strategies require spatially high-resolution climate input data in which the error with respect to observations has been previously corrected. To quantify the impact of bias correction, we examine the effects of quantile-mapping bias correction on the climate change signal (CCS) of climate, extremes, and biological variables from the convective regional climate model COSMO-CLM and two dynamic vegetation models (LPJ-GUESS and CARAIB). COSMO-CLM was driven and analyzed at 3 km horizontal resolution over Central Europe (CE) and the Iberian Peninsula (IP) for the transient period 1980–2070 under the RCP8.5 scenario. Bias-corrected and uncorrected climate simulations served as input to run the dynamic vegetation models over Wallonia. Main result of the impact of bias correction on the climate is a reduction of seasonal absolute precipitation by up to −55% with respect to uncorrected simulations. Yet, seasonal climate changes of precipitation and also temperature are marginally affected by bias correction. Main result of the impact of bias correction on changes in extremes is a robust spatial mean CCS of climate extreme indices over both domains. Yet, local biases can both over- and underestimate changes in these indices and be as large as the raw CCS. Changes in extremely wet days are locally enhanced by bias correction by more than 100%. Droughts in southern IP are exacerbated by bias correction, which increases changes in consecutive dry days by up to 14 days/year. Changes in growing season length in CE are affected by quantile mapping due to local biases ranging from 24 days/year in western CE to −24 days/year in eastern CE. The increase of tropical nights and summer days in both domains is largely affected by bias correction at the grid scale because of local biases ranging within ±14 days/year. Bias correction of this study strongly reduces the precipitation amount which has a strong impact on the results of the vegetation models with a reduced vegetation biomass and increases in net primary productivity. Nevertheless, there are large differences in the results of the two applied vegetation models.

For the problem that the traceability parameters of sudden water pollution are difficult to determine, a fast traceability model based on a simplified mechanistic model coupled with an optimization algorithm is proposed to improve the accuracy of sudden water pollution traceability. In this paper, according to the diffusion law of pollutants, a quantitative formula of pollutant diffusion is proposed, and the differential calculation process of the pollutant convection equation is optimized. The Dynamic Programming and Beetle Antennae Search algorithm (DP-BAS) with dynamic step size is used in the reverse optimization process, which can avoid the problem of entering the local optimal solution in the calculation process. The DP-BAS is used to inverse solve the quantization equation to realize the decoupling of pollutant traceability parameters, transforming the multi-parameter coupled solution into a single-parameter solution, reducing the solution dimension, and optimizing the difficulty and solution complexity of pollutant traceability. The proposed traceability model is applied to the simulation case, the results show that the mean square errors of pollutant placement mass, location, and time are 2.39, 1.16, and 1.19 percent, respectively. To further verify the model reliability, the Differential Evolution and Markov Chain Monte Carlo simulation method (DE-MCMC) as well as Genetic Algorithms (GA) were introduced to compare with the proposed model to prove that the model has certain reliability and accuracy.

Utilizing the digital economy’s contribution to green total factor productivity is a key strategy for accelerating China’s green growth, although more research is still needed to understand the mechanism of this influence. This study uses panel data from 282 Chinese prefecture-level cities from 2011 to 2019 to empirically assess the impact of the digital economy and city size on GTFP. First, GTFP overall exhibits an upward trend with excellent spatial correlation and minimal regional variation. Second, the findings demonstrate that, while surrounding locations’ GTFP is not affected by the digital economy, local productivity can be improved. Third, the heterogeneity study demonstrates that the digital economy contributes more to local GTFP in the eastern region compared to the central and western regions, with the central region making the largest contribution to GTFP in the surrounding regions; the first, second, and third tier cities have more contributions from the digital economy to local and neighboring GTFP than the fourth and fifth tier cities. Fourth, city size positively modifies the relationship between the green total factor productivity and the digital economy. The western region is where the positive moderating effect of city size expansion is greatest. Moreover, compared to first-, second-, and third-tier cities, the fourth- and fifth-tier cities have a stronger beneficial moderating effect of city size increase. In light of this, we should focus on the growth of the digital economy, optimize city scale, and fully exploit the scale effect produced by the concentration of the digital industries and the spillover effect produced by the spread of the digital technology.

As we mark one year since the start of the Russia-Ukraine war, countries and companies alike continue to adapt to this unprecedented disruption in the global economy and the subsequent uncertainty. One aspect that has not been thoroughly addressed from this conflict is its effect on companies’ ESG ratings and how the decision to remain or withdraw from Russia influences these ratings. To study this, a panel regression methodology on ESG data was applied on a significant number of companies before and after the start of the conflict. According to the results obtained, it would seem that insofar neither the overall ESG scores, nor the Social Scores are influenced by companies’ decisions to leave or to stay in Russia after 24-th of February 2022. We consider that these are not final outcomes and it will require further investigations and methodology improvements. The paper provides insights for ESG ratings providers, regulators and asset managers on the effects of companies’ decision to withdraw from/remain in an invading country on ESG ratings.

Urbanization and industrialization in developing countries has contributed to great changes in rural settlements, which presents an increasing threat to rural sustainability. Spatiotemporal changes in rural settlements at the county level are significant to land use planning and are not clear in the highly urbanized regions. This study considered Jiangsu, one of the most urbanized provinces of China, as an example and investigated the spatial variation in rural settlements and their socioeconomic driving factors during the period of 2000–2020 using mixed geographic weighted regression. The results showed that the area of rural settlements in the highly urbanized province expanded from 2000 to 2015 following a decrease in the rural population, but then began to decrease from 2015. There were obvious spatial differences in the rural settlements in the counties of Jiangsu Province. The area of rural settlements in the different counties maintained a positive association with the rural population and cropland but had a negative correlation with the rural production value in 2000. By 2020, the area of rural settlements was only positively associated with the rural population. The correlation between the area of rural settlements and rural population continually decreased from 2000 to 2020. The area of rural settlements had no significant association with the area of urban settlements. The expansion of rural settlements mainly occurred at the expense of cropland. The decrease in the rural settlements was accompanied by an increase in the urban settlements and an expansion of cropland. The policy implications arising from this study are presented to provide guidance for rural development at the county level and ensure rural sustainability.

The primary objective of the present study is to identify the asymmetric relationship between green finance, trade openness, and foreign direct investment with environmental sustainability. The existing research utilizes the asymmetric approach to evaluate annual data from 1980 to 2021. The findings of this study show heterogeneous results. Therefore, the outcomes of the study confirm the nonlinear (NARDL) association between the variables in Pakistan. Moreover, the study describes the positive shock of foreign direct investment (FDI) as a significant and positive relationship with environmental degradation, while the negative shock of FDI shows a negative and significant relationship with the environment. Furthermore, the study scrutinizes the positive shock of green finance as a significant and negative relationship with environmental degradation; the negative shocks also show a negative relationship with environmental degradation in Pakistan. In addition, the consequences of the study suggest that the government should implement taxes on foreign investment and that investors should use renewable energy to produce goods. Furthermore, the results suggest that the government should utilize fiscal policy and fiscal funds to enhance carbon-free projects. Moreover, green securities should be used for green technologies. However, Pakistan can control its carbon emissions and achieve the target of a sustainable environment. Therefore, Pakistan’s government should stabilize its financial markets and introduce carbon-free projects. Furthermore, the main quantitative achievement according to the outcomes suggests that policymakers make policies in which they suggest to the government to control foreign investment that causes carbon emissions because of trade openness and also invest the funds in renewable energy, which helps to control the carbon emissions.

During their journey of developing, resource-based cities gradually deplete the resources on which they rely for survival. Scientific and reasonable research on the industrial and ecological aspects of resource-based cities is conducive to the coordinated development of cities. In order to further analyze the industrial structure of resource-based cities systematically and analyze the comprehensive level of resource-based cities from multi-dimensional perspective. This paper took 8 resource-based cities in Sichuan Province as the research object, and constructed the index system from two systems: industrial structure and ecological environment, then the shift-share analysis, entropy weight method and capacity coupling coefficient model were used to analyze their level of industrial structure, ecological environment and the coupling relationship respectively. According to the results of the study, it can be concluded that the main influencing factor in the development of industrial structure is the industrialmix effect, while the ecological level presents a decreasing level due to the lack of control of total industrial solid waste and energy consumption. The coupling degree between industrial structure and ecological environment in resource-based cities in Sichuan Province is relatively stable, and the coupling coordination degree also gradually tends to a stable state. In the subsequent development, the focus should be on the coal mining and dressing industry and the power, heat production and supply industry. Starting with the actual industrial structure of resource-based cities and specific indicators that affected the ecological environment, this paper hereby analyzed the development momentum and unified and coordinated development status of resource-based cities. The main purpose of this paper is providing some technical support for resource-based cities to improve their coordinated urban development, and giving policy suggestions for the coordinated development of resource-based cities.

Deforestation poses a major threat to the tropical montane forest ecosystems of East Africa. Montane forests provide key and unique ecological and socio-economic benefits to the local communities and host diverse flora and fauna. There is evidence of ongoing deforestation and forest clearance in these montane forests although estimates diverge among different sources suggesting rates of 0.4%–3% yr−1. Quantifying deforestation rates and forest disturbance is critical to design conservation and sustainable management policies for forest management. This study quantified the rate of deforestation and forest recovery over the last three decades for the Mau Forest Complex and Mount Elgon forests in Kenya and Uganda using Landsat time-series satellite imagery. With the analysis, classification accuracies of 86.2% and 90.5% (kappa 0.81 and 0.88) were achieved for the Mau Forest Complex and the Mt Elgon forests, respectively. 21.9% (88,493 ha) of the 404,660 ha of Mau forest was lost at an annual rate of −0.82% yr−1 over the period between 1986 and 2017. More positively, 18.6% (75,438 ha) of the forest cover that was disturbed during the same period and is currently undergoing recovery. In Mt Elgon forest, 12.5% (27,201 ha) of 217,268 ha of the forest cover was lost to deforestation at an annual rate of −1.03% yr−1 for the period between 1984–2017 and 27.2% (59,047 ha) of the forest cover disturbed is undergoing recovery. The analysis further demonstrated agriculture (both smallholder and commercial) was the main driver of forest cover loss in Mau forest, accounting for 81.5% (70,612 ha) of the deforestation, of which 13.2% was due to large scale and 68.3% was related to the smallholders. For the Mt Elgon forest, agriculture was also the main driver accounting for 63.2% (24,077 ha) of deforestation followed by the expansion of human settlements that contributed to 14.7% (5,597 ha) of forest loss. This study provides accurate and novel estimates of the rate of deforestation for the Mau forest complex and Mt Elgon forest ecosystems. These rates are higher than previously estimated and recent deforestation has been identified, which provides a quantitative basis for forest restoration programs and to design conservation policies.

Introduction: Traditional methods to estimate exposure to PM2.5 (particulate matter with less than 2.5 µm in diameter) have typically relied on limited regulatory monitors and do not consider human mobility and travel. However, the limited spatial coverage of regulatory monitors and the lack of consideration of mobility limit the ability to capture actual air pollution exposure.Methods: This study aims to improve traditional exposure assessment methods for PM2.5 by incorporating the measurements from a low-cost sensor network (PurpleAir) and regulatory monitors, an automated machine learning modeling framework, and big human mobility data. We develop a monthly-aggregated hourly land use regression (LUR) model based on automated machine learning (AutoML) and assess the model performance across eight metropolitan areas within the US.Results: Our results show that integrating low-cost sensor with regulatory monitor measurements generally improves the AutoML-LUR model accuracy and produces higher spatial variation in PM2.5 concentration maps compared to using regulatory monitor measurements alone. Feature importance analysis shows factors highly correlated with PM2.5 concentrations, including satellite aerosol optical depth, meteorological variables, vegetation, and land use. In addition, we incorporate human mobility data on exposure estimates regarding where people visit to identify spatiotemporal hotspots of places with higher risks of exposure, emphasizing the need to consider both visitor numbers and PM2.5 concentrations when developing exposure reduction strategies.Discussion: This research provides important insights for further public health studies on air pollution by comprehensively assessing the performance of AutoML-LUR models and incorporating human mobility into considering human exposure to air pollution.

Reasonable application of organic-inorganic soil conditioners can effectively improve the structure and fertility of reclaimed soil in abandoned homestead. Aggregate stability is an important indicator to evaluate soil structure and fertility, and is largely influenced by soil internal forces (van der Waals attractive force, electrostatic repulsive force, hydration repulsive force) and particle surface properties. However, there are few studies on the influence of different soil conditioners on the reclaimed soil internal forces and its relationship with the aggregate stability. Therefore, we selected six different treatments of organic fertilizer (TO), fly ash (TF), maturing agent (TM), maturing agent + organic fertilizer (TMO), fly ash + organic fertilizer (TFO) and control (CK) to conduct a 5-year field experiment to study the effects of reclaimed soil particle interaction forces and surface characteristics on aggregate stability under the treatment of different soil conditioners. The results showed that with the application of soil conditioners, the soil organic matter (SOM), specific surface area (SSA), surface charge (σ0), cation exchange capacity (CEC), aggregate mean weight diameter (MWD) and Hamaker constant increased gradually, while the pH value decreased slightly. In particular, the MWD under the treatments of TFO and TMO increased by 150.3% and 65.6% respectively compared with that under the CK treatment. With the increasing application of soil conditioners, the electrostatic repulsive force and van der Waals attractive force between reclaimed soil particles increased constantly, but the net resultant force between particles decreased and the net attractive force increased continuously, thus improving the aggregate stability. Therefore, there is a significant negative correlation between the net resultant force among reclaimed soil particles and MWD and CEC. In addition, 10−2 mol L-1 is the critical concentration that affects the reclaimed soil internal force, and the organic-inorganic treatments of TFO and TMO can improve the net resultant force better. In a word, the particle interaction forces are important factors affecting the reclaimed soil structural stability, and this study provides a scientific reference for the rational selection of soil conditioners and its interaction force mechanism in the reclaimed soil improvement.

Hawaiian coastal wetlands provide important habitat for federally endangered waterbirds and socio-cultural resources for Native Hawaiians. Currently, Hawaiian coastal wetlands are degraded by development, sedimentation, and invasive species and, thus, require restoration. Little is known about their original structure and function due to the large-scale alteration of the lowland landscape since European contact. Here, we used 1) rapid field assessments of hydrology, vegetation, soils, and birds, 2) a comprehensive analysis of endangered bird habitat value, 3) site spatial characteristics, 4) sea-level rise projections for 2050 and 2100 and wetland migration potential, and 5) preferences of the Native Hawaiian community in a GIS site suitability analysis to prioritize restoration of coastal wetlands on the island of Molokaʻi. The site suitability analysis is the first, to our knowledge, to incorporate community preferences, habitat criteria for endangered waterbirds, and sea-level rise into prioritizing wetland sites for restoration. The rapid assessments showed that groundwater is a ubiquitous water source for coastal wetlands. A groundwater-fed, freshwater herbaceous peatland or “coastal fen” not previously described in Hawaiʻi was found adjacent to the coastline at a site being used to grow taro, a staple crop for Native Hawaiians. In traditional ecological knowledge, such a groundwater-fed, agro-ecological system is referred to as a loʻipūnāwai (spring pond). Overall, 39 plant species were found at the 12 sites; 26 of these were wetland species and 11 were native. Soil texture in the wetlands ranged from loamy sands to silt and silty clays and the mean % organic carbon content was 10.93% ± 12.24 (sd). In total, 79 federally endangered waterbirds, 13 Hawaiian coots (‘alae keʻokeʻo; Fulica alai) and 66 Hawaiian stilts (aeʻo; Himantopus mexicanus knudseni), were counted during the rapid field assessments. The site suitability analysis consistently ranked three sites the highest, Kaupapaloʻi o Kaʻamola, Kakahaiʻa National Wildlife Refuge, and ʻŌhiʻapilo Pond, under three different weighting approaches. Site prioritization represents both an actionable plan for coastal wetland restoration and an alternative protocol for restoration decision-making in places such as Hawaiʻi where no pristine “reference” sites exist for comparison.

Agricultural ecosystem is the largest artificial ecosystem on Earth and provide 66% of the world’s food supply. Soil microorganisms are an engine for carbon and nutrient cycling. However, the driving mechanism of soil microbial community structure and carbon and nitrogen transformation mediated by fertilization and planting pattern in rainfed agricultural ecosystems is still unclear. The research was conducted at the Changwu Agricultural Ecology Experimental Station in Shaanxi Province, China. Seven different fertilization and planting pattern were designed. The Phosphate fatty acids (PLFAs) were used to explore the effects of fertilization and plating pattern on the soil microbial community structure and the relationship with soil carbon and nitrogen transformation. The results showed that there were significant differences in soil physical and chemical properties among treatments. Organic fertilizer significantly increased the soil carbon and nitrogen and decreased the soil pH. The contents of total PLFAs and microbial groups in the wheat and corn rotation treatment were the highest. Compared with the change in planting pattern, organic fertilizer had a greater impact on PLFA content and soil ecological processes. The soil microbial community structure has a significantly positive correlation with soil organic carbon (SOC), total carbon (TC), total nitrogen (TN), and total phosphorus (TP). Compared with applying NP fertilizer, applying organic fertilizer significantly increased the soil respiration rate and mineralized nitrogen content while decreasing the soil microbial biomass carbon (MBC). The correlation analysis showed that soil respiration was significantly positively correlated with SOC and TP, and mineralized nitrogen was significantly positively correlated with SOC, nitrate nitrogen, TN and MBC. Structural equation modeling (SEM) showed that the soil respiration rate was significantly positively affected by TC and negatively affected by SWC and explained 63%, whereas mineralized nitrogen was significantly positively influenced by TN and explained 55% of the total variance.

The Basin of Mexico is an endorheic lacustrine basin with an outstanding ecological and social history. There is evidence that it hosted human settlers since the late Pleistocene. This basin was home to great antique civilizations and many endemic species of flora and fauna. The main lake in the Basin was the Great Lake of Mexico, which was divided into five lakes and provided goods and services to the native communities. After the Spanish conquest, a rule was established to drain the lakes to prevent flooding in the city. The naturalist Alexander von Humboldt visited Mexico City in the early 1800s, and carried out the first formal scientific water quality analysis of the lakes of the basin. The Basin of Mexico gone through serious modifications due to urbanization and changes of land use reducing the lacustrine area to the virtual extinction of the lakes. The lakes are currently reduced to wetlands accounting for only 2.83% of the former lake and receiving mainly treated wastewater discharges. We carried out a comparative study between Humboldt’s results and the current characteristics of water from these lake remnants analyzed with the same methods that he used. In addition, we assessed several morphometric parameters and performed water quality assessments using modern methods. Changes in water quality characteristics and ionic composition were detected, with Xochimilco being the lake with the highest water quality score and Texcoco and Chalco showing major alterations. The drastic reduction in the area of the remaining water bodies and the modifications in their water quality are discussed.

Algal genomics research contributes to a deeper understanding of algal evolution and provides useful genomics inferences correlated with various functions. Published algal genome sequences are very limited owing to genome assembly challenges. Because genome data of freshwater algae are rapidly increasing with the recent boom in next-generation sequencing and bioinformatics, an interface to store, interlink, and display these data is needed. To provide a substantial genomic resource specifically for freshwater algae, we developed the Freshwater Algae Database (FWAlgaeDB), a user-friendly, constantly updated online repository for integrating genomic data and annotation information. This database, which includes information on 204 freshwater algae, allows easy access to gene repertoires and gene clusters of interest and facilitates potential applications. Three functional modules are integrated into FWAlgaeDB: a Basic Local Alignment Search Tool tool for similarity analyses, a Search tool for rapid data retrieval, and a Download function for data downloads. This database tool is freely available at http://www.fwalagedb.com/#/home. To demonstrate the utility of FWAlgaeDB, we also individually mapped metagenomic sequencing reads of 10 water samples to FWAlgaeDB and Nt algae databases we constructed to obtain taxonomic composition information. According to the mapping results, FWAlgaeDB may be a better choice for identifying algal species in freshwater samples, with fewer potential false positives because of its focus on freshwater algal species. FWAlgaeDB can therefore serve as an open-access, sustained platform to provide genomic data and molecular analysis tools specifically for freshwater algae.

As the core of “Rise of Central China” strategy, the regional innovation capacity of the Wuhan Metropolitan Area is the key to enhance the innovation ability of central China and even China. High-tech industries are the key driving force to improving regional innovation. Studying the relationship between high-tech clusters and regional innovation capacity helps optimize the spatial layout of regional high-tech industries, upgrade the industrial structure and enhance regional innovation capacity. Based on the panel data of nine cities in the Wuhan Metropolitan Area from 2010–2019, we measure the regional innovation capacity and the degree of high-tech cluster using the super-SBM and locational quotient. Furthermore, we explore the high-tech cluster’s influence on regional innovation capacity by constructing a non-linear panel threshold model and a spatial econometric model. The results showed: 1) The innovation capability of the Wuhan Metropolitan Area shows a “W” type fluctuation upward trend, and the degree of the high-tech cluster is below the quotient level of 1, showing a continuous “M” type trend; 2) There is a non-linear double-threshold effect between high-tech cluster and innovation capacity, and the overall effect of promotion, but there is a marginal decreasing, probably because of the crowding effect or over-competition of the high-tech cluster in some regions; 3) After considering the spatial effect, the impact of the high-tech cluster on the innovation capacity of both local and neighboring regions shows a “U” curve, but the spillover to the neighboring areas is relatively limited. Therefore, to give full play to the advantages of the high-tech cluster, it is necessary to take a long-term view when formulating relevant industrial policies while considering the differences in regional economic development levels and spatial spillover effects.