Coastal Cities Research Articles

Planning For Resilience: Examining Gaps in Cyclone Preparedness and Impacts on Marginalized Communities in India's East coast cities

Cyclones and associated storm surges pose a major threat to coastal communities in India, especially along the Bay of Bengal where their intensity is higher. This paper examines cyclone resilience planning in four coastal cities - Visakhapatnam, Puri, Rameshwaram, and Velankanni. A multi-dimensional analysis is undertaken to focus on disaster preparedness, tourism economy contributions, socio-economic inclusion, and sustainability principles. The key objective is to study the integration of cyclone resilience with equitable, tourism-based development to strengthen vulnerable communities through conceptual modeling. The research methodology employs a mixed methods approach combining literature review, secondary data analysis of resilience infrastructure and tourism statistics, and conceptual model development. Findings highlight continued infrastructure and preparedness gaps alongside persisting socio-economic disparities limiting resilience access for marginalized groups. While progress is visible on early warnings, shelters, and evacuations, constraints exist regarding storm-proofing of amenities, customized enterprise support, secure housing, and risk financing tools. The study concludes that resilient, sustainable outcomes require planning processes that engage tourism-dependent communities through bottom-up participation. Coupled with hazard-resilient land use standards, customized livelihood protection schemes, resilient affordable housing programs, and coherent governance; tourism-centric coastal cities can enhance capacities to absorb cyclone impacts and enable inclusive rebuilding. Key Words: Cyclone resilience, Coastal city planning, Socio-economic vulnerabilities, Tourism economies

Refined Sea Salt Markers for Coastal Cities Facilitating Quantification of Aerosol Aging and PM2.5 Apportionment.

Sea salt (ss) aerosols in PM2.5 are often quantified through source apportionment by applying sodium (Na+) and chloride (Cl-) as the markers, but both markers can be substantially emitted from anthropogenic sources. In this study, we differentiate ss from nonss (nss) portions of Na+ and Cl- to better apportion PM2.5 in a coastal tropical urban environment. Size-resolved ionic profiles accounting for Cl- depletion of aged ss were applied to 162-day measurements during 2012 and 2018-2019. Results show that the nss (likely anthropogenic) portions, on average, account for 50-80% of total Na+ and Cl- in submicron aerosols (PM1). This corresponds to up to 2.5 μg/m3 of ss in submicron aerosols that can be ∼10 times overestimated if one attributes all Na+ and Cl- in PM1 to ss. Employing the newly speciated ss- and nss-portions of Na+ and Cl- to source apportionment of urban PM2.5 via positive matrix factorization uncovers a new source of transported anthropogenic emissions during the southwest monsoon, contributing to 12-15% of PM2.5. This increases anthropogenic PM2.5 by ≥19% and reduces ss-related PM2.5 by >30%. In addition to demonstrating Cl- depletion (aging) in submicron aerosols and quantifying ssNa+, nssNa+, ssCl-, as well as nssCl- therein, the refined PM2.5 apportionment resolves new insights on PM2.5 of anthropogenic origins in urban environments, useful to facilitate policy making.

ICEEMDAN-Informer-GWO: a hybrid model for accurate wind speed prediction.

Extensive research has been diligently conducted on wind energy technologies in response to pressing global environmental challenges and the growing demand for energy. Accurate wind speed predictions are crucial for the effective integration of large wind power systems. This study presents a novel and hybrid framework called ICEEMDAN-Informer-GWO, which combines three components to enhance the accuracy of wind speed predictions. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) component improves the decomposition of wind speed data, the Informer model provides computationally efficient wind speed predictions, and the grey wolf optimisation (GWO) algorithm optimises the parameters of the Informer model to achieve superior performance. Three different sets of wind speed prediction (WSP) models and wind farm data from Block Island, Gulf Coast, and Garden City are used to thoroughly assess the proposed hybrid framework. This evaluation focusses on WSP for three specific time horizons: 5minutes, 30minutes, and 1hour ahead. The results obtained from the three conducted experiments conclusively demonstrate that the proposed hybrid framework exhibits superior performance, leading to statistically significant improvements across all three time horizons.

Coastal Flood Induced Salinity Intrusion Risk Assessment Using a Spatial Multi-criteria Approach in the South-Western Bangladesh

AbstractBangladesh is extremely vulnerable to sea-level rise and other climate-induced extreme events, such as flooding, storm surge, and salinity intrusion. The south-western coastal region of Bangladesh is particularly vulnerable to salinity intrusion caused by cyclone induced storm surges and coastal floods. Salinity intrusion endanger land productivity by increasing both soil and surface water salinity. Detailed risk assessment using spatial mapping approach can contribute to mitigating the effects of salinity intrusion on natural capital and the environment. In this study, we established and evaluated a spatial multi-criteria approach for mapping the risk levels of areas to salinity intrusion impacts using field data and geospatial techniques at the local scale. We evaluated the viability of the proposed approach using Khulna District, a major coastal city and saline prone area in the south-western Bangladesh. We considered three risk components (i.e. vulnerability, exposure and hazard) with 16 relevant criteria for the study. For each criterion, an Analytical Hierarchy Process (AHP) was used to build and weight spatial raster map layers. Individual maps for each risk component were generated using a weighted sum technique, and lastly, a risk map was created by combining those. Our generated maps correctly identified relevant spatial dimensions as well as risk levels (i.e. very-high to very-low). The outcomes of our study suggest that the southern (east and west) parts of the study area are mostly susceptible to salinity intrusion due to higher storm surge impacts, lower elevation, and land use patterns than other parts. We validate our findings using a qualitative and quantitative approach. We believe that this novel approach would be useful to create risk maps that policymakers and relevant stakeholders could potentially use to evaluate risks posed by flood induced salinity intrusion in coastal regions of Bangladesh and elsewhere with similar geo-climatic context.

Flood risk evaluation of the coastal city by the EWM-TOPSIS and machine learning hybrid method

The frequent occurrence of floods and waterlogging has significantly impacted coastal cities. Effective mapping of flood risk can enhance the precision of disaster risk reduction strategies. However, there is room for improvement in fields such as flood inundation data, evaluation objectivity, and classification refinement. This study, using Xiamen as a case study, advanced flood inundation data accuracy by generating a frequency-based flood inundation map from multi-year remote sensing imagery. This study integrated the EWM-TOPSIS model with a neural network model to evaluate the flood risk. The EWM-TOPSIS model was employed to assess the flood vulnerability and flood exposure, while the multi-class neural network model to simulate the flood hazard. This combined approach reduced subjectivity in the flood risk assessment of Xiamen and achieved a finer level of risk classification compared to traditional binary neural networks. The results indicated that the flood vulnerable areas of Xiamen are concentrated along waterways distant from roads, with high flood exposure in Tong'an and Xiang'an districts. The flood-prone areas in Xiamen are primarily located along the coastal areas characterized by extensive impermeable surfaces. High flood risk areas are mainly distributed in Tong'an and Xiang'an subdistricts, particularly in Xiangping and Xindian. The method developed offers support for accurate flood risk identification and decision-making in cities with limited data resources.

Quantifying the Impact of Sea Level on Coastal Cities using Bayesian optimized Monte Carlo Simulation model

Quantifying the Impact of Sea Level on Coastal Cities using Bayesian optimized Monte Carlo Simulation model

Anthropogenic sources and air mass transport affect spatial and seasonal variations of ambient halocarbons in southeastern China

Halocarbons play a vital role in ozone depletion and global warming, and are regulated by the Montreal Protocol (MP) and its amendments. China has been identified as an important contributor to the halocarbon emissions, but the regional sources of halocarbons in China are not yet well comprehended. To investigate the characteristics, emissions, and source profiles, this study conducted a field campaign in Xiamen, a coastal city in southeastern China. Higher enhancements were found in the unregulated halocarbons (CH3Cl, CH2Cl2, CHCl3) than in the MP (Montreal Protocol) eliminated species (CCl4, CH3Br) and the MP controlled species (HCFCs, HFCs). Many of the measured halocarbons varied seasonally and regionally, depending on the anthropogenic sources and atmospheric transport. Backward trajectory analysis showed that the air masses from inland were polluted over Shandong, Hebei, and northern Fujian in the cold season, while the air masses from the sea in the warm season were clean. Different air masses in two seasons were associated with the halocarbon patterns in the study area. Industrial activities, especially solvent usage, were the primary sources of halocarbons, the emission hot spots in Fujian Province were concentrated in Sanming, Fuzhou, and Xiamen, and the unregulated halocarbons made the largest contribution. This study provides an insight for a deep understanding of the characteristics and potential sources of halocarbons, and for strengthened management of halocarbons.

Techno-economic evaluation of a seawater source heat pump system with auxiliary heat source in cold region of China

The utilizing of renewable seawater energy through seawater source heat pumps (SWHPs) in coastal areas is considered as a sustainable technology with minimal environment impact. Promotion of this technology needs clear understanding of its techno-economic performance but there are limited relevant studies. An empirical study was conducted in Qingdao, China, based on a practical installation comprising SWHPs supplemented by electric boilers. On-site measurements revealed considerable COP of 3.81 for SWHP and COPs of 2.87 for the whole system, implying SWHP as a viable and efficient solution for space heating in mild winter in this region. Results from TRNSYS simulations showed SWHP with auxiliary gas boilers (SWHP-GB) achieved the lowest annual primary energy demand and best economic and environmental benefits compared with other three schemes, namely SWHP with electric boilers (SWHP-EB), chillers with gas boilers (Chiller-GB) and air source heat pumps with gas boilers (ASHP-GB). SWHP played important roles in energy efficiency of the whole system with a considerable seasonal COP of 3.72 and 3.22 respectively for heating and cooling. Annual primary energy demand of SWHP-GB accounted for 55 %, 78 % and 56 % of SWHP-EB, Chiller-GB and ASHP-GB, respectively. SWHP-GB achieved the lowest life cycle costs and a rapid payback period of 10.9 years. Moreover, using direct or indirect heat exchange with seawater was found with limited impact on techno-economic performance of the system. To conclude, SWHP-GB could be used as an efficient solution for building heating and cooling in northern coastal cities in China.

Potentially toxic elements in urban soils of the coastal city of the Sea of Azov: levels, sources, pollution and risk assessment

Potentially toxic elements in urban soils of the coastal city of the Sea of Azov: levels, sources, pollution and risk assessment

Climate risk assessment and adaption ability in China's coastal urban agglomerations - A case study of Guangdong-Hong Kong-Macao greater bay area

As climate change intensifies, extreme weather events, especially the coastal flooding is becoming increasingly severe. Carbon emissions exacerbate climate change, leading to increased frequency and intensity of extreme precipitation, which may result in flooding. This study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), a coastal city cluster in China, and adopts the scenario analysis approach to evaluate flood risks in the different carbon mitigation scenario from the perspectives of dangerousness, and adaption ability. The result shows that the Huizhou in the east of the GBA and the Zhaoqing in the northwest have the highest level of flood disaster risk. However, the central region of the GBA (Guangzhou, Dongguan, Zhongshan, and Foshan) is very vulnerable to disaster-inducing environments and hazard-affected bodies, which means that the flooding risk and the impact of such risk in the GBA display regional differentiation characteristics and the spatial patterns of them are different. According to the carbon neutrality scenario, the flood risk in the GBA peaks in 2030, and decreases during 2030–2090. In contrast, under the uneven development and high carbon pathways, the risk peaks in 2060, and the overall risk by 2090 is roughly the same as that in 2060. In addition, the regions with the highest flood disaster risk are Guangzhou, Foshan, Dongguan and Zhongshan along the Pearl River estuary. Overall, it shows that areas with higher levels of urbanization and more developed economies will face higher flooding risks. The “dangerousness-adaption ability” structure can reflect the spatial distribution pattern of flood disaster risks, identify the priority areas for flood prevention and control, and is conducive to risk management. At the same time, the research results show that ecological protection have a positive effect on reducing flood risk. In addition, corresponding policy recommendations for pre-disaster, mid-disaster and post-disaster have been proposed, providing references for flood risk management in the GBA.

Application of Ensemble Algorithm Based on the Feature-Oriented Mean in Tropical Cyclone-Related Precipitation Forecasting

Tropical cyclones (TCs) are characterized by robust vortical motion and intense thermodynamic processes, often causing damage in coastal cities as they result in landfall. Accurately estimating the ensemble mean of TC precipitation is critical for forecasting and remains a foremost global challenge. In this study, we develop an ensemble algorithm based on the feature-oriented mean (FM) suitable for spatially discrete variables in precipitation ensembles. This method can adjust the locations of ensemble precipitation fields to reduce the location-related deviations among ensemble members, ultimately enhancing the ensemble mean forecast skill for TC precipitation. To evaluate the feasibility of the FM in TC precipitation ensemble forecasting, 18 landing TC cases in China from 2019 to 2021 were selected for validation. For precipitation forecasts of the landing TCs with a varying leading time, we conducted a comprehensive quantitative evaluation and comparison of the precipitation forecast skills of the FM and arithmetic mean (AM) algorithms. The results indicate that the field adjustment algorithm in the FM can effectively align with the TC precipitation structure and the location of the ensemble mean, reducing the spatial divergence among precipitation fields. The FM method demonstrates superior performance in the equitable threat score, probability of detection, and false alarm ratio compared with the AM, exhibiting an overall improvement of around 10%. Furthermore, the FM ensemble mean shows a higher pattern of the correlation coefficient with observations and has a smaller root mean square error than the AM ensemble mean, signifying that the FM method can better preserve the characteristics of the precipitation structure. Additionally, an object-based diagnostic evaluation method was used to verify forecast results, and the results suggest that the attribute distribution of FM forecast objects more closely resembles that of observed precipitation objects (including the area, longitudinal and latitudinal centroid locations, axis angle, and aspect ratio).

Post‐Harvest Travels of Marine Fish: How Small Fish Food Systems Variously Support Food Security and Nutrition in Coastal and Inland Cities in Ghana

AbstractThis paper enquires into the travels of low‐cost varieties of marine fish in the context of Ghana, distinguishing flows that move toward coastal cities versus those destined for distant, inland cities. It derives data from field research on the Ghanaian small fish food system through surveys, FGDs and interviews conducted in Accra and Tamale. It is argued that although both coastal and inland cities partake in small fish food systems, they experience different flow patterns and FSN outcomes. These are partly related to the fact that Ghana relies on the confluence of local and imported sources for the delivery of low‐cost fish varieties to the population, especially in urban regions where such demand is high. We recommend that policies should focus on investing in infrastructure, especially transport and distribution systems linking cities to fishing communities, to enhance low‐cost fish distribution and improve FSN outcomes for low‐income urban consumers in Ghana.

Overview and Analysis of Ground Subsidence along China’s Urban Subway Network Based on Synthetic Aperture Radar Interferometry

Deformation along a subway rail network is related to the safe operation of the subway and the stability of construction facilities on the surface, making long-term deformation monitoring imperative. Long-term monitoring of surface deformation along the subway network and statistical analysis of the overall deformation situation are lacking in China. Therefore, targeting 35 Chinese cities whose subway mileage exceeds 50 km, we extracted their surface deformation along subway networks between 2018 and 2022, using spaceborne synthetic aperture radar (SAR) interferometry (InSAR) technology and Sentinel-1 satellite data. We verified the results with the continuous global navigation satellite system (GNSS) stations’ data and found that the root mean square error (RMSE) of the InSAR results was 3.75 mm/year. Statistical analysis showed that ground subsidence along the subways was more prominent in Beijing, Tianjin, and other areas in the North China Plain, namely Kunming (which is dominated by karst landforms), as well as Shanghai, Guangzhou, Qingdao, and other coastal cities. In addition, an analysis revealed that the severity of surface subsidence correlated positively with a city’s gross domestic product (GDP) with a Pearson correlation of 0.787, since the higher the GDP, the more frequent the construction and maintenance of subway, and the more commuters there are, which in turn exacerbates the disturbance to the surface. Additionally, the type of land cover also affects the ground deformation. Our findings provide a reference for constructing, operating, and maintaining the urban subway systems in China.

Predicting dust pollution from dry bulk ports in coastal cities: A hybrid approach based on data decomposition and deep learning

Dust pollution from storage and handling of materials in dry bulk ports seriously affects air quality and public health in coastal cities. Accurate prediction of dust pollution helps identify risks early and take preventive measures. However, there remain challenges in solving non-stationary time series and selecting relevant features. Besides, existing studies rarely consider impacts of port operations on dust pollution. Therefore, a hybrid approach based on data decomposition and deep learning is proposed to predict dust pollution from dry bulk ports. Port operational data is specially integrated into input features. A secondary decomposition and recombination (SDR) strategy is presented to reduce data non-stationarity. A dual-stage attention-based sequence-to-sequence (DA-Seq2Seq) model is employed to adaptively select the most relevant features at each time step, as well as capture long-term temporal dependencies. This approach is compared with baseline models on a dataset from a dry bulk port in northern China. The results reveal the advantages of SDR strategy and integrating operational data and show that this approach has higher accuracy than baseline models. The proposed approach can mitigate adverse effects of dust pollution from dry bulk ports on urban residents and help port authorities control dust pollution.

Quantifying high-resolution carbon emissions driven by land use change in the Guangdong-Hong Kong-Macao Greater Bay Area

Accurate research on carbon emission spatial distribution is vital for achieving carbon neutrality. Previous studies suffered from scale effects, demanding enhanced resolution. This study collected and computed land use carbon emission data for the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). It also introduced a novel method to downscale this data into 30 m raster format using night light and NDVI data. The study showed a consistent annual rise in GBA land use carbon emissions, accompanied by increasing disparities. At the city level, emissions in the highest-ranking cities were 58 times higher than those in the lowest-ranking ones. Impervious land carbon emissions held over 90% sway over total carbon emissions, making them the primary driver of land use-related carbon emissions. Remarkably, high-emission zones, such as coastal cities Guangzhou, Foshan, Dongguan, and Hong Kong, expanded outward, forming multi-tiered, high-density carbon areas, giving rise to an “∩” shape pattern. Carbon emission change rates from cropland were modest, characterized by a pattern of fluctuation. Moreover, spatial autocorrelation and K-means cluster analyses uncovered a noticeable spatial correlation among urban land use carbon emissions in the GBA. Consequently, a compelling case exists for enhancing inter-city cooperation to collectively pursue regional emission reduction objectives.

Assessment of the accessibility of public transport in the Tricity (Poland): analytical use of geographical information systems (GIS) in the context of selected public transport measures

ABSTRACT The Tricity consists of three coastal cities – Gdańsk, Gdynia and Sopot – which also form the core of the Tricity agglomeration. They are among the main development centres of the country. Proper access to public transport for residents is crucial for the sustainable development of the agglomeration. Although the cities are connected by functional links in different areas, the further development of a coherent public transport system is problematic. It is therefore crucial to formulate the question of how these cities can develop the public (collective) transport network? Do residents have access to it and does it meet their needs? Are these measures complementary? A proprietary methodological approach using QGIS and ORS tools was used to conduct the survey. The results of the survey show that 83.5% of Tri-City residents have the nearest train, bus, tram or trolleybus stop within a 5-minute walk of their place of residence. This is followed by 14.39% of residents having a stop within 10 min, 1.47% within 15 min and 0.20% within 20 min. Unfavourable time accessibility applies to 0.45% of residents. The findings of the study may help Tricity local authorities to develop a sustainable public transport policy.

A comprehensive assessment of wind energy potential and wind farm design in a coastal industrial city

Purpose Jubail Industrial City is one of the largest industrial centers in the Middle East, offering potential opportunities for renewable energy generation. This research paper presents a comprehensive analysis of the wind resources in Jubail Industrial City and proposes the design of a smart grid-connected wind farm for this strategic location. Design/methodology/approach The study used wind data collected at three different heights above ground level – 10, 50 and 90 m – over four years from 2017 to 2020. Key parameters, such as average wind speeds (WS), predominant wind direction, Weibull shape, scale parameters and wind power density (WPD), were analyzed. The study used Windographer, an exclusive software program designed to evaluate wind resources. Findings The average WS at the respective heights were 3.07, 4.29 and 4.58 m/s. The predominant wind direction was from the north-west. The Weibull shape parameter (k) at the three heights was 1.77, 2.15 and 2.01, while the scale parameter (c) was 3.36, 4.88 and 5.33 m/s. The WPD values at different heights were 17.9, 48.8 and 59.3 W/m2, respectively. Originality/value The findings suggest that Jubail Industrial City possesses favorable wind resources for wind energy generation. The proposed smart grid-connected wind farm design demonstrates the feasibility of harnessing wind power in the region, contributing to sustainable energy production and economic benefits.

Machine learning for water demand forecasting: case study in a Brazilian coastal city

Abstract Water resources management is crucial for human well-being and contemporary socio-economic development. However, the increasing use of water has led to various problems that affect its quality and availability. To address these issues, accurate forecasting of water consumption is essential for the optimal operation of water collection, treatment, and distribution systems. This study aims to compare four machine learning methods for predicting daily urban water demand in a Brazilian coastal tourist city (Guaratuba – Paraná). Historical data from the city’s water distribution system, spanning from 2016 to 2019 (1,461 measurements in total), were considered along with meteorological and calendar data to conduct the investigation. Three time series cross-validation approaches were considered for each method, thus totaling 12 evaluation settings. All models were subjected to hyperparameter optimization and evaluated using appropriate performance metrics from the literature. Results demonstrate the importance of using nonlinear models to predict short-term water demand, highlighting the problem’s complexity. From the compared models, multilayer perceptron provided the best results. Finally, regardless of the model, the best results were obtained by applying an expanding window time series cross-validation, indicating that the more historical data available, the better, in this particular case.

Decomposing the comprehensive efficiency of major cities into divisions on governance, ICT and sustainability: network slack-based measure model

The rapid urbanization and population growth in major cities have given rise to enduring urban challenges, prompting local governments to assess urban development performance and customize urban planning strategies to meet these challenges. This study aims to formulate an evaluation framework for the comprehensive efficiency of major cities, centering on governance, Information and Communication Technology (ICT), and sustainability as fundamental aspects. Distinct from prior research, the framework employs the network slack-based measure (SBM) to unravel efficiency, concurrently scrutinizing input/output slack and link efficiency. Utilizing the network SBM model, the study assesses the comprehensive operational efficiencies of 38 major Chinese cities spanning from 2015 to 2019. The application of the Kruskal–Wallis test discerns differences in comprehensive efficiency between coastal and inland areas. The results indicate that governance, ICT, and sustainability significantly influence the comprehensive operations of cities. In addition to general and short-term policies, the implementation of the National New-Type Urbanization Plan by the Chinese central government has led to an increase in the number of cities demonstrating sustainable efficiency. Despite fluctuations in the efficiency gap during the study period, coastal cities consistently outperform their inland counterparts in terms of efficiency. Persistent geographical disparities underscore the imperative for balanced development. While acknowledging positive strides in sustainable urbanization, the study emphasizes the ongoing necessity to address the adverse impacts of urban development, positioning governance, ICT, and sustainability as indispensable elements in confronting the multifaceted challenges inherent in urban development. The research findings contribute significantly to the field of urban efficiency evaluation, accentuating the collaborative impact of governance, ICT, and sustainability.

Study on River Protection and Improvement Based on a Comprehensive Statistical Model in a Coastal Plain River Network

When considering the contradictions between river management and protection in a typical plain river network, it is always confirmed that the river area has usually been encroached upon due to the development of human society. Based on the analysis of multiple attributes of the river network, a statistical model has been proposed in this study in order to determine the river network protection indices such as river area ratio, storage capacity and flux. In this study, a numerical method is proposed to improve the structure and connectivity of the river network by calculating the occupation and supplement balance. According to the principle of water area dynamic balance, the river network structure and its connectivity are improved through water area adjustment in a typical coastal city. As the simulation results show, the water surface ratio equals 8.17%, the storage capacity equals 112.6 million m3 and the water flux equals to 656.06 m3/s in the selected study area. The flood drainage capacity is introduced as the priority function, other functions are also improved due to river management and protection. The harmonious and sustainable coexistence between human society and the river network is then promoted. This comprehensive statistical model proved to be a good tool for the coastal area to enhance the comprehensive attributes of the coastal plain river network and the sustainable development of the local area in the future.