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With continued sea level rise and over-exploitation, saline water extends farther inland, causing changes in soil salinity and water quality and leading to permanent land salinization and ecosystem damage. Saltwater intrusion (SWI), causing numerous ecosystem problems and disasters, brings risk to urban ecosystems in coastal cities. Ecological risk, in the Greater Bay Area in China, should be assessed based on the effect of SWI status on ecosystem health. In this study, we built a new ecological risk-assessment model based on the geographic information system (GIS) technique and spatial data. At the conceptual level, four main stressors were identified based on literature reading and fieldwork. Four stress factors (SFs) were thoroughly investigated, namely, SF1: the intensity gradient immersed in saltwater; SF2: the mountain phreatic water supply; SF3: the salinity tolerance of urban greenbelt vegetation; and SF4: the supply capacity of irrigation water to suppress saline water. After a comprehensive evaluation using GIS and the analytic hierarchy process (AHP), we mapped and assessed the ecological risk level of the urban greenbelt for the SWI. Our results showed that the area of urban green space affected by the SWI was approximately 49.31 km2, almost 12.05%. Ecological risk was sorted into five ranks: (1) very low risk 47.53%, (2) low risk 26.29%, (3) medium risk 22.92%, (4) high risk 2.45%, and (5) very high risk (0.8%). The ecological infrastructure of sponges should include freshwater conservation in coastal cities, and more attention should be paid to fresh groundwater discharge from coastal ecosystems in Shenzhen.

China aims to improve its system for pursuing environmental damage compensation environmental violation cases. To hold violators accountable, China has an effective system for the identification and assessment (I&A) of environmental damage. This study selected a typical case of the illegal dumping of solid waste (IDSW) in China to analyze the causes, the degree, and characteristics of environmental damage, focusing on components such as the physical quantification and valuation of damage. The findings were as follows: (1) Compensation claimants and obligors consider baseline damage confirmation and causality analysis key components of I&A. (2) The I&A process for a specific case needs to focus on key nodes such as the type, location, and duration of IDSW. (3) Restraining IDSW requires accurately quantifying the physical and value-related losses caused by solid waste dumping. (4) In the selected case study, the damage from environmental contamination caused by the IDSW incident amounted to 3938,990 yuan, including an environmental damage value of 3651,990 yuan and a transaction cost of 287,000 yuan. Both parties accepted the I&A calculation process in this case, and the desired punishment effect was achieved. Hence, the case study demonstrated that accurate I&A is the technical basis for environmental damage compensation. Thus, in the future, more attention should be paid to the role of scientific and technological means and knowledge reserves in the I&A of environmental damage.

Ocean water transparency is an important indicator to measure the productivity of marine ecosystems, and it has great impact on phytoplankton biomass and community structure. Secchi disk depth (Zsd) is a traditional measure of water transparency. With the development of satellite remote sensing technology, inversion through empirical model or semi-analytical model has become an effective and efficient means to obtain ocean Zsd. In recent years, ocean parameter inversions based on water classification become an ongoing trend. Jia et al. (2021) constructed a fuzzy-logic optical water type scheme (i.e., U-OWT) for the global oceans and multi satellite sensors, which was a potential water classification template for remote sensing inversion of ocean Zsd. Using U-OWT as the water classification basis, and by collecting 613 global ocean distributed SeaWiFS (Sea-viewing Wide Field-of-view Sensor) Rrs and Zsd (with the range of 0.3–44.3 m) in-situ matchups, this study explored the optimal Zsd empirical models for each optical water type and each water trophic state respectively. As a result, a class-based Zsd hybrid empirical model (abbreviated as the Zsd-OWT model) was developed, which had relative high inversion accuracy over the entire Zsd range (MAPE = 8.25 %, RMSE = 0.65 m, bias = – 0.03 m). Specifically, the Zsd-OWT model conducted the global empirical inversion for the oligotrophic water classes and the class-based hard fusion empirical inversions for the mesotrophic and the eutrophic water classes. Although the Zsd-OWT model was designed for SeaWiFS sensor, it still has the potential to be migrated to other multispectral sensors with similar band settings.

With continuous rises in GDP, land cover in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) has undergone a drastic change over the period 1990–2018. In this study, land use in the GBA was divided into six types: farmland, forestland, grassland, wetland, construction land, and unused land. We analyzed changes in spatiotemporal patterns according to region and type by using statistical analysis, spatial clustering, and hotspot analysis, focusing on the spatial characteristics of areas where land-use types changed with high frequency. The high-frequency land use in the GBA has strategic guidance for further urban planning and management. With discussions on urban planning, the natural environment, and social and economic development, we found the following: (1) Urban construction land in the GBA showed a unipolar growth mode, increasing from 5.63% to 14.34% from 1990 to 2018. Accordingly, the degree of urban concentration and contiguity rose continuously. (2) Hotspots with frequent land-use changes were concentrated mainly in areas with economic intensity. (3) Plots with high-frequency land-use changes (Flc > 2) were concentrated primarily in the waters and rivers of the GBA within 10 km of the administrative boundaries of prefecture-level cities. (4) Nearly 80% of the land has been or will be transformed into ecological land over the period 1990–2018. On the basis of these findings, we suggest further improving land-use efficiency, and ecological land damage and the over-occupation of sea space should be avoided while maintaining economic growth. Thus, linking increases and decreases in construction land is an excellent land-consolidation mechanism to transform inefficient urban land into ecological land.

Recreational green space (RGS) offers the most intuitive place for urban residents to get in touch with nature. The service radiation of RGS is related to the travel mode, however, residents’ travel behavior has long been ignored in the study of RGS services. This paper considers the Guangdong–Hong Kong–Macau Greater Bay Area (GBA), uses multi-source data, refines the spatial distribution of residents, extracts and classifies the RGS into three categories (township (TRGS), country (CRGS), and urban (URGS)), and analyzes the spatial distribution of the three types of RGS. Using the travel isochrone, the RGS services coverage (including spatial and population coverage) in 11 cities within the GBA is defined by multiple travel modes. Finally, a comprehensive evaluation of the RGS services in the GBA is conducted based on the residents’ transportation choice willingness and recreational selection. The results showed that: (1) TRGSs are mainly distributed in the suburbs, URGSs are mainly concentrated in the mid-western and southern regions, and CRGSs are mostly concentrated in the center of the GBA. (2) For daily travel (15-min and 30-min travel modes), the coverage of the RGS services is unevenly distributed, while under the 60-min travel mode, the RGS services can almost fully cover the residents in the GBA (SPURGS > 99%). (3) The RGS service of the central cities (Hong Kong, Macau) is better than that of the edge cities (Zhaoqing, Jiangmen), and the different city types should adopt different RGS planning and management strategies. This study provides a reference for RGS refined planning and maintenance in mega-urban agglomerations.

Urban greenery has positive impacts on the well-being of residents and provides vital ecosystem services. A quantitative evaluation of full-view green coverage at the human scale can guide green space planning and management. We developed a still camera to collect hemisphere-view panoramas (HVPs) to obtain in situ heterogeneous scenes and established a panoramic green cover index (PGCI) model to measure human-scale green coverage. A case study was conducted in Xicheng District, Beijing, to analyse the quantitative relationships of PGCI with the normalized difference vegetation index (NDVI) and land surface temperature (LST) in different land use scenarios. The results show that the HVP is a useful quantization tool: (1) the method adaptively distinguishes the green cover characteristics of the four functional areas, and the PGCI values are ranked as follows: recreational area (29.6) > residential area (19.0) > traffic area (15.9) > commercial area (12.5); (2) PGCI strongly explains NDVI and LST, and for each unit (1%) increase in PGCI, NDVI tends to increase by 0.007, and (3) LST tends to decrease by 0.21 degrees Celsius. This research provides government managers and urban planners with tools to evaluate green coverage in complex urban environments and assistance in optimizing human-scale greenery and microclimate.

The classification of natural waters is a way to generalize and systematize ocean color science. However, there is no consensus on an optimal water classification template in many contexts. In this study, we conducted an unsupervised classification of the PACE (Plankton, Aerosols, Cloud, and Ocean Ecosystem) synthetic hyperspectral data set, divided the global ocean waters into 15 classes, then obtained a set of fuzzy logic optical water type schemes (abbreviated as the U-OWT in this study) that were tailored for several multispectral satellite sensors, including SeaWiFS, MERIS, MODIS, OLI, VIIRS, MSI, and OLCI. The consistency analysis showed that the performance of U-OWT on different satellite sensors was comparable, and the sensitivity analysis demonstrated the U-OWT could resist a certain degree of input disturbance on remote sensing reflectance. Compared to existing ocean-aimed optical water type schemes, the U-OWT can distinguish more mesotrophic and eutrophic water classes. Furthermore, the U-OWT was highly compatible with other water classification taxonomies, including the trophic state index, the multivariate absorption combinations, and the Forel-Ule Scale, which indirectly demonstrated the potential for global applicability of the U-OWT. This finding was also helpful for the further conversion and unification of different water type taxonomies. As the fundamental basis, the U-OWT can be applied to many oceanic fields that need to be explored in the future. To promote the reproducibility of this study, an IDL®-based standalone U-OWT calculation tool is freely distributed.

Noise pollution is one of the major urban environmental pollutions, and it is increasingly becoming a matter of crucial public concern. Monitoring and predicting environmental noise are of great significance for the prevention and control of noise pollution. With the advent of the Internet of Things (IoT) technology, urban noise monitoring is emerging in the direction of a small interval, long time, and large data amount, which is difficult to model and predict with traditional methods. In this study, an IoT-based noise monitoring system was deployed to acquire the environmental noise data, and a two-layer long short-term memory (LSTM) network was proposed for the prediction of environmental noise under the condition of large data volume. The optimal hyperparameters were selected through testing, and the raw data sets were processed. The urban environmental noise was predicted at time intervals of 1 s, 1 min, 10 min, and 30 min, and their performances were compared with three classic predictive models: random walk (RW), stacked autoencoder (SAE), and support vector machine (SVM). The proposed model outperforms the other three existing classic methods. The time interval of the data set has a close connection with the performance of all models. The results revealed that the LSTM network could reflect changes in noise levels within one day and has good prediction accuracy. Impacts of monitoring point location on prediction results and recommendations for environmental noise management were also discussed in this paper.

As cyanobacteria blooms occur in many types of inland water, routine monitoring that is fast and accurate is important for environment and drinking water protection. Compared to field investigations, satellite remote sensing is an efficient and effective method for monitoring cyanobacteria blooms. However, conventional remote sensing monitoring methods are labor intensive and time consuming, especially when processing long-term images. In this study, we embedded related processing procedures in Google Earth Engine, developed an operational cyanobacteria bloom monitoring workflow. Using this workflow, we measured the spatiotemporal patterns of cyanobacteria blooms in China’s Taihu Lake from 2000 to 2018. The results show that cyanobacteria bloom patterns in Taihu Lake have significant spatial and temporal differentiation: the interannual coverage of cyanobacteria blooms had two peaks, and the condition was moderate before 2006, peaked in 2007, declined rapidly after 2008, remained moderate and stable until 2015, and then reached another peak around 2017; bays and northwest lake areas had heavier cyanobacteria blooms than open lake areas; most cyanobacteria blooms primarily occurred in April, worsened in July and August, then improved after October. Our analysis of the relationship between cyanobacteria bloom characteristics and environmental driving factors indicates that: from both monthly and interannual perspectives, meteorological factors are positively correlated with cyanobacteria bloom characteristics, but as for nutrient loadings, they are only positively correlated with cyanobacteria bloom characteristics from an interannual perspective. We believe reducing total phosphorous, together with restoring macrophyte ecosystem, would be the necessary long-term management strategies for Taihu Lake. Our workflow provides an automatic and rapid approach for the long-term monitoring of cyanobacteria blooms, which can improve the automation and efficiency of routine environmental management of Taihu Lake and may be applied to other similar inland waters.

Street greenery is a component of urban green infrastructure. By forming foundational green corridors in urban ecological systems, street greenery provides vital ecological, social, and cultural functions, and benefits the wellbeing of citizens. However, because of the difficulty of quantifying people’s visual perceptions, the impact of street-visible greenery on housing prices has not been fully studied. Using Beijing, which has a mature real estate market, as an example, this study evaluated 22,331 transactions in 2014 in 2370 private housing estates. We selected 25 variables that were classified into three categories—location, housing, and neighbourhood characteristics—and introduced an index called the horizontal green view index (HGVI) into a hedonic pricing model to measure the value of the visual perception of street greenery in neighbouring residential developments. The results show that (1) Beijing’s homebuyers would like to reside in residential units with a higher HGVI; (2) Beijing’s homebuyers favour larger lakes; and (3) Beijing’s housing prices were impacted by the spatial development patterns of the city centre and multiple business centres. We used computer vision to quantify the street-visible greenery and estimated the economic benefits that the neighbouring visible greenery would have on residential developments in Beijing. This study provides a scientific basis and reference for policy makers and city planners in road greening, and a tool for formulating street greening policy, studying housing price characteristics, and evaluating real estate values.

Urban perceptions are related to residents’ subjective well-being, activities, settlement choice, and physical and mental health, which are vital for promoting sustainable community development. Landsenses ecology proposes the concepts of physical and psychological perceptions to measure the interaction between humans and the urban environment. However, such perceptions and their quantitative relationships with housing rental prices (HRPs) are still not clear. In this study, a novel urban-perception-oriented hedonic price model (UP-HPM) is proposed based on the construction of a multisource geographic database of Beijing’s rental market with rich information on people-oriented perception metrics, neighborhood environment characteristics, and HRPs. Furthermore, we use advanced machine learning (ML) pipelines to measure residents’ multidimensional urban perceptions and investigate their impact on block-level HRPs and their spatial disparities. We also quantify and compare the contribution of different physical and psychological perceptual attributes to HRPs through UP-HPM. The results show that neighborhood perceived greenery and the use of fences tend to bring about positive psychological perceptions and further affect HRPs. By comparing different combinations of hedonic and perceptual features, the UP-HPM performs well in terms of fitting capacity, spatial description, and interpretability and can inspire humanistic thinking and landsenses planning guidelines for the regulation of the allocation of public rental housing and improvement in block-level physical settings and infrastructure services, thereby providing insights into how best to promote residents’ quality of life and well-being. [ABSTRACT FROM AUTHOR]

The environmental impact of consumption and production is diverse and wide-reaching: air, water and ground pollutants are emitted during different phases of a life cycle, natural resources are overexploited and ecosystems are degraded. Changing consumption patterns in Asia forecast major impacts from increased demand for electric and electronic goods, cars and processed and protein-rich food, as well as buildings. While some countries have adopted policy at a strategic level to promote sustainable consumption and production, most countries, in particular developing countries in the Asia-Pacific region, do not have a comprehensive policy on sustainable consumption and production but would have much to gain from building on this new approach. The aim of this paper is to elaborate on current theory and practice in the area of sustainable production and consumption focusing on sustainable urban development. The paper will: 1) analyse current policy thinking in the field of regional sustainable consumption and production; 2) identify issues for regional policy development in the same fields; and 3) propose regional public policy related to sustainable consumption and production patterns, such as improved energy efficiency and use of alternative energy. [ABSTRACT FROM AUTHOR]

Remote sensing was used to assess the impacts of tourism development on temporal land-cover changes in the Lugu Lake region, home to the Mosuo people. The ecological and economic significance of the Lugu Lake area derives from the existence of a unique matriarchal system and the success of tourism development. Temporal land-cover changes between 1990 and 2005 were evaluated using digital interpretation of multi-temporal Landsat TM images. Pairwise comparison methods were used to quantify changes in land-cover during three periods: 1990 to 1995, 1995 to 2001, and 2001 to 2005. The areas surveyed in each period were 10,226 ha, 7,727 ha and 9,344 ha, respectively. The annual rate of land-cover change for farmland, forest, grassland and wetland were 2.86%, 5.85%, 3.95%, 6.28%, respectively. Farmland and wetland have decreased, whereas forest, grassland and residential areas have increased. The land-cover changes could be explained by the success of tourism development and ecosystem management in Lugu Lake region. The impact of tourism on land cover and the community environment were assessed. The results show that most farmers actively left farms for off-farm jobs and took measures to protect forest, grassland and wetland by developing tourism in the study area. However, construction in residential areas has proceeded in a disorderly fashion. In future, the potential impacts of tourism on the lake ecosystem need to be re-assessed and monitored. [ABSTRACT FROM AUTHOR]

It is important and necessary for sustainable management of regional ecosystems to establish a framework for dynamic monitoring of ecosystem change (DMEC). For sustainable ecosystem management of the Lugu Lake region, the key issues addressed in DMEC are monitoring range, principles, intervals, site and indices, which can be used to create a framework for long-term DMEC for a region. This paper puts forward a new method to combine remote sensing with field investigations to achieve accurate and appropriate changes in a regional ecosystem. Because regional ecosystem change is closely related to local inhabitants, the authors also consider the participation of these people. Finally, the use of DMEC to provide a framework for decision-making and sustainable management of a regional ecosystem is described. [ABSTRACT FROM AUTHOR]