Search Results (18)

Surface ozone pollution poses a significant threat to human health and ecosystems. However, its highly variable spatiotemporal distribution, especially at hourly scales across China, complicates effective risk management. This variability presents substantial challenges for accurate estimation and forecasting, underscoring the importance of evaluating current hourly surface ozone estimation methods. Therefore, this study collaboratively evaluated the performance of chemical transport model simulations and satellite-based estimates of hourly surface ozone concentrations over mainland China in 2019. Using data from 3185 ground monitoring stations operated by the Ministry of Ecology and Environment, as well as six independent observation sites in Hong Kong, Xianghe, Nam Co, Akedala, Longfengshan, and Waliguan, this study found that both datasets exhibited systematic biases and lacked spatiotemporal consistency. The Community Atmosphere Model with Chemistry simulation results exhibited an average relative bias of 23.17%, generally overestimated ozone concentrations in high-altitude regions, but outperformed the satellite-based estimates at the independent sites, while consistently underestimating ozone concentrations in densely populated urban areas. In contrast, the satellite-based estimates performed better in regions with dense monitoring sites, with mean biases typically within 10% of observations, but their accuracy was limited in remote areas due to sparse ground-based calibration. It is particularly noteworthy that both datasets showed deficiencies in capturing extremely high-value events, nighttime ozone variations, and dynamic transport processes, underscoring challenges in the representation of photochemical processes in the model and in the design of satellite estimation algorithms. The results highlight the importance of optimizing model parameterization schemes, improving satellite estimation algorithms, and integrating multi-source data to enhance the accuracy and stability of hourly ozone estimates. This study provides multi-scale quantitative insights into the relative strengths and limitations of different ozone estimation methods, laying a solid scientific foundation for future data integration, regional air quality management, and policy development. Full article

Energy generated from wind (in the form of wind farms (WFs)) is expected to help alleviate rising energy demand in Saudi Arabia, driven by industrial development and population growth. However, before implementing wind farms, conducting a comprehensive wind resource assessment (WRA) study is of paramount importance. This paper presents the analysis of the wind resource potential of a site in Yanbu city, which is located on the western coastal area of Saudi Arabia, using a comprehensive study. The hourly data on wind speed and direction over a one-year period was used in the presented analysis. The plant capacity factor (CF) and annual energy production (AEP) are evaluated for more than 100 commercial wind turbines (WTs). The highest AEP was achieved by the ‘Enercon E126/7.5 MW’ turbine, generating 14.49 GWh, with a corresponding CF of 21.82%. In contrast, the lowest AEP was observed for the ‘Northern Power d’ turbine, producing only 0.13 GWh, with a CF of 14.89%. The highest CF was recorded for the ‘Leitwind LTW104/2.0 MW’ turbine at 40.67%, corresponding to an AEP of 7.12 GWh. The results obtained are very valuable for designers in selecting the appropriate WT to obtain the predicted AEP and CF with the appropriate turbine class. Furthermore, this study applied the K-means clustering algorithm to classify WTs into three distinct categories. Building on this classification, synthetic datasets representing tailored WT configurations were generated—a novel methodology that enables the simulation of site-specific designs not yet available in existing market offerings. These datasets equip wind farm developers with the ability to define WT specifications for manufacturers, guided by two key criteria: the site’s wind resource profile and the target performance metrics of the WT. Full article

Previous studies assessing population exposure to heat stress have focused primarily on environmental heat loads without accounting for variations in human thermo–physiological responses to heat. A novel 30-year (1991–2020) human thermal bioclimate dataset, consisting of hourly mPET (modified physiologically equivalent temperature) values for diverse populations, was employed in the present study to assist in addressing this gap. Focusing on the Athens urban area (AUA), Greece, the climatology and long-term trends in acclimatization-based strong heat stress (accliSHS) experienced by average male and female adult and senior individuals during the warm period of the year (April–October) were investigated. Results showed that an average adult (senior) in AUA experienced, on average, approximately 13 (18) additional days with at least 1 h accliSHS in 2020 compared with 1991. The increasing rates per year were particularly pronounced for days with ≥6 h accliSHS, indicating a rise in the daily duration of heat stress in AUA from 1991 to 2020. Combining the variations in climate and demographics in AUA during the examined 30-year period, the long-term trends in ≥1 h accliSHS exposure for the study population types were further examined. This analysis revealed that seniors’ exposure to ≥1 h accliSHS in AUA increased by up to +153,000 person-days $\times$ year⁻¹ from 1991 to 2020. Increasing population aging was the main driver of this outcome, highlighting the urgent need for heat–health action planning in Greece. Full article

This article investigates viable solutions to implement an Urban Air Mobility network in Milan, Italy, and analyzes its influence on the airspace capacity. The network comprises eight vertiports for passenger transport among two main airports in the area and the city using electric vertical take-off and landing aircraft (eVTOLs). A Fast-Time Simulation (FTS) model with the software AirTOp (Air Traffic Optimization) allowed the evaluation of the ideal capacity of the network by varying two configurations, which differ from each other in terms of the number of Final Approach and Takeoff areas (FATOs). The results show how it is possible to reach high hourly capacities (in the order of one hundred), thus allowing the use of the service for about 4% of the total passengers passing through the two airports during the reference day chosen for this study. However, the results are ideal due to the strong idealism of the system, which overlooks several factors, and they should be considered as the maximum limit that can be obtained. Despite this, the method presented in this article can also be adapted for other urban areas with high population densities. In addition, the use of a simulation tool of this type allows, in addition to a numerical analysis, a qualitative analysis of the network behavior in terms of traffic, thus highlighting the criticalities of the proposed systems. Full article

Human mobility data are crucial for transportation planning and congestion management. However, challenges persist in accessing and using raw mobility data due to privacy concerns and data quality issues such as redundancy, missing values, and noise. This research introduces an innovative GIS-based framework for creating individual-level long-term spatio-temporal mobility data at a city scale. The methodology decomposes and represents individual mobility by identifying key locations where activities take place and life patterns that describe transitions between these locations. Then, we present methods for extracting, representing, and generating key locations and life patterns from large-scale human mobility data. Using long-term mobility data from Shanghai, we extract life patterns and key locations and successfully generate the mobility of 30,000 virtual users over seven days in Shanghai. The high correlation (R² = 0.905) indicates a strong similarity between the generated data and ground-truth data. By testing the combination of key locations and life patterns from different areas, the model demonstrates strong transferability within and across cities, with relatively low RMSE values across all scenarios, the highest being around 0.04. By testing the representativeness of the generated mobility data, we find that using only about 0.25% of the generated individuals’ mobility is sufficient to represent the dynamic changes of the entire urban population on a daily and hourly resolution. The proposed methodology offers a novel tool for generating long-term spatiotemporal mobility patterns at the individual level, thereby avoiding the privacy concerns associated with releasing real data. This approach supports the broad application of individual mobility data in urban planning, traffic management, and other related fields. Full article

At highly populated areas, such as the Greater Athens Area in Greece, the air quality differs significantly from one municipality to another, being highly affected by the local anthropogenic sources (traffic, residential heating, navigation) and consequent emissions. Thus, the existence of a dense network of low-cost air quality sensors provides evidence of seasonal patterns on particulate concentrations within the urban zone. In the present study, hourly PM_2.5 concentrations recorded using low-cost sensors at six municipalities in the Greater Athens Area (Vrilissia, Psychiko, Peristeri, Rentis, Agia Varvara, Palaio Faliro) with different characteristics (population density, pollutant sources, surrounding land use) were collected for the year 2022. The highest mean seasonal values were recorded in the western and southern suburbs (Peristeri, Rentis and Palaio Faliro) during the cold period (winter of the year 2022). Mean concentrations decreased significantly in spring; the mean concentrations were 31.3 μg/m³ and 18.5 μg/m³ in Vrilissia in winter and spring, respectively (year: 2022). Full article

Climate change has significantly increased human thermal stress, particularly in tropical regions, exacerbating associated risks and consequences, such as heat-related illnesses, decreased workability, and economic losses. Understanding the changes in human thermal stress and its drivers is crucial to identify adaptation measures. This study aims to assess various meteorological variables’ spatial and seasonal impact on Wet Bulb Globe Temperature (WBGT), an indicator of human thermal stress, in Peninsular Malaysia. The Liljegren method is used to estimate WBGT using ERA5 hourly data from 1959 to the present. The trends in WBGT and its influencing factors are evaluated using a modified Mann-Kendall test to determine the region’s primary driver of WBGT change. The results indicate that air temperature influences WBGT the most, accounting for nearly 60% of the variation. Solar radiation contributes between 20% and 30% in different seasons. Relative humidity, zenith, and wind speed have relatively lesser impacts, ranging from −5% to 20%. Air temperature has the highest influence in the northern areas (>60%) and the lowest in the coastal regions (40%). On the other hand, solar radiation has the highest influence in the southern areas (20–40%) and the least in the north. The study also reveals a significant annual increase in temperature across all seasons, ranging from 0.06 to 0.24 °C. This rapid temperature rise in the study area region has led to a substantial increase in WBGT. The higher increase in WBGT occurred in the coastal regions, particularly densely populated western coastal regions, indicating potential implications for public health. These findings provide valuable insights into the factors driving WBGT and emphasize the importance of considering air temperature as a key variable when assessing heat stress. Full article

The process of urbanization resulting from population growth is causing a transformation of natural landscapes into built environments, and contributing to a significant rise in air and surface temperatures in urban areas, resulting in what is known as the urban heat island (UHI). Ignoring the UHI effect and use of weather data from open fields and airport locations for energy and thermal comfort analysis can lead to over- and underestimation of heating and cooling loads, improper sizing of equipment, inefficiencies in the mechanical systems operation, and occupants’ thermal discomfort. There is a need for computationally efficient urban canopy temperature prediction models that account for the urban morphology and characteristics of the study area. This paper presents the development and application of an artificial neural network (ANN)-based method for generating hourly urban canopy temperature and local wind speed for energy simulation. It was used to predict the urban canopy temperature of a neighborhood in downtown Vancouver and the resulting building energy consumption and indoor temperature in a typical building in the area. The results showed that the UHI effect increased the total cooling energy demand by 23% and decreased the total heating energy consumption by 29%, resulting in an overall negative effect on the total energy demand of the building, which was 18% higher in the urban area. The UHI effect also increased the number of hours of indoor temperature above the cooling set point by 7.6%. The methodology can be applied to determine the urban canopy temperature of neighborhoods in different climate zones and determine the varying urban heat island effects associated with the locations. Full article

Most remote islands are characterized by small populations, many of whom live under the poverty line, poor geographical accessibility and lack of electricity. As such, the solar still, which has a low capital cost, easy operation and less need of maintenance, is recommended to be used in remote islands possessing rich solar irradiance. Against this backdrop, the present study aimed to design and fabricate an effective solar still suitable for application in the remote islands with low freshwater sources but easy access to sea water and rich solar irradiance. Integrating a conventional passive double-slope solar still with an evacuated solar water heater, fins and wick material improves the heat transfer rate through the brine in the basin and increases effective evaporative surface area. Experiments were conducted using batch mode operation during the periods September to October 2021 for the passive solar stills and November 2021 for the active solar still. Experimental results reveal that the augmentation of fins, wicks and a solar water heater influences the overall distillate output of the solar still. The combined use of fins, wicks and a solar water heater increases the average daily productivity by 147% and the average daytime hourly productivity by 245% compared to the conventional passive solar still under similar average solar radiation levels. Using the present design, the active solar still under the solar environment of the testing location can provide 4.4 L of potable water per day. However, to achieve the minimum requirement of 7.5 L/day per person set by WHO, the present design should be modified by increasing the absorber area of the active solar still by 63% and adding eight more evacuated tubes to the solar water heater. The estimated cost per liter of potable water generated by the active (modified) solar still showed that bottled water sold in a typical remote county (Penghu) in Taiwan was 117–283% more expensive than the water generated by the still. Full article

Increasing air temperature coupled with high humidity due to ongoing climate change across most parts of South Africa is likely to induce and intensify heat exposure, particularly in densely populated areas. The adverse health implications, including heatstroke, are expected to be common and more severe during extreme heat and heat wave events. The present study was carried out to examine heat stress conditions and long-term trends in South Africa. The study aimed to identify geographical locations exposed to elevated heat stress based on over two decades of hourly ground-based data. Selected heat stress indicators were calculated based on Steadman’s apparent temperature (AT in °C). The trends in AT were assessed based on the non-parametric Mann–Kendall (MK) trend test at 5% significance level. Positive trends were detected in 88% of the selected weather stations except in Welkom-FS, Ficksburg-FS, Langebaanweg-WC, Lambertsbaai Nortier-WC, Skukuza-MP, and Thabazimbi-LP. Approximately 47% of the detected positive trends are statistically significant at 5% significant level. Overall, high climatological annual median (ATmed) values (>32 °C) were observed at 42 stations, most of which are in low altitude regions, predominately along the coastlines. The hottest towns with ATmed values in the danger category (i.e., 39–50 °C) were found to be Patensie-EC (41 °C), Pietermaritzburg-KZN (39 °C), Pongola-KZN (39 °C), Knysna-WC (39 °C), Hoedspruit-LP (39 °C), Skukuza-MP (45 °C), and Komatidraai-MP (44 °C). The results provide insight into heat stress characteristics and pinpoint geographical locations vulnerable to heat stress conditions at the community level in South Africa. Such information can be useful in monitoring hotspots of heat stress and contribute to the development of local heat–health adaptation plans. Full article

Rapid demographic ageing is a global challenge and has tremendous implications for transportation planning, because the mobility of elderly people is an essential element for active ageing. Although many studies have been conducted on this issue, most of them have been focused on aggregated travel patterns of the elderly, limited in spatiotemporal analysis, and most importantly primarily relied on sampled (2–3%) household travel surveys, omitting some trips and having concerns of quality and credibility. The objectives of this study are to present more in-depth analysis of the elderly’s spatiotemporal activity and travel behaviors, to compare them with other age and gender groups, and to draw implications for sustainable transportation for the elderly. For our analysis, we used locational trajectory-based mobile phone data in Gangnam, Korea. The data differs from sampled household travel survey data, as mobile phone data represents the entire population and can capture comprehensive travelers’ movements, including peculiarities. Consistent with previous researches, the results of this study showed that there were differences in activity and travel patterns between age and gender groups. However, some different results were obtained as well: for instance, the average nonhome activity time per person for the elderly was shorter than that of the nonelderly, but the average numbers of nonhome activities and trips were rather higher than those of nonelderly people. The results of this study and advantage of using mobile phone data will help policymakers understand the activities and movements of the elderly and prepare future sustainable transportation. Full article

Estimating and mapping population distributions dynamically at a city-wide spatial scale, including those covering suburban areas, has profound, practical, applications such as urban and transportation planning, public safety warning, disaster impact assessment and epidemiological modelling, which benefits governments, merchants and citizens. More recently, call detail record (CDR) of mobile phone data has been used to estimate human population distributions. However, there is a key challenge that the accuracy of such a method is difficult to validate because there is no ground truth data for the dynamic population density distribution in time scales such as hourly. In this study, we present a simple and accurate method to generate more finely grained temporal-spatial population density distributions based upon CDR data. We designed an experiment to test our method based upon the use of a deep convolutional generative adversarial network (DCGAN). In this experiment, the highest spatial resolution of every grid cell is 125125 square metre, while the temporal resolution can vary from minutes to hours with varying accuracy. To demonstrate our method, we present an application of how to map the estimated population density distribution dynamically for CDR big data from Beijing, choosing a half hour as the temporal resolution. Finally, in order to cross-check previous studies that claim the population distribution at nighttime (from 8 p.m. to 8 a.m. on the next day) mapped by Beijing census data are similar to the ground truth data, we estimated the baseline distribution, first, based upon records in CDRs. Second, we estimate a baseline distribution based upon Global Navigation Satellite System (GNSS) data. The results also show the Root Mean Square Error (RMSE) is about 5000 while the two baseline distributions mentioned above have an RMSE of over 13,500. Our estimation method provides a fast and simple process to map people’s actual density distributions at a more finely grained, i.e., hourly, temporal resolution. Full article

A methodological framework for the estimation of the expected value of hourly peak water demand factor and its dependence on the spatial aggregation level is presented. The proposed methodology is based on the analysis of volumetric water meter measurements with a 1-h time aggregation, preferred by water companies for monitoring purposes. Using a peculiar sampling design, both a theoretical and an empirical estimation of the expected value of the peak factor and of the related standard error (confidence bands) are obtained as a function of the number of aggregated households (or equivalently of the number of users). The proposed methodology accounts for the cross-correlation among consumption time series describing local water demand behaviours. The effects of considering a finite population is also discussed. The framework is tested on a pilot District Metering Area with more than 1000 households equipped with a telemetry system with 1-h time aggregation. Results show that the peak factor can be expressed as a power function tending to an asymptotic value greater than one for the increasing number of aggregated households. The obtained peak values, compared with several literature studies, provide useful indications for the design and management of secondary branched pipes of water distribution systems. Full article

PM_2.5 is a main source of China’s frequent air pollution. Using real-time monitoring of PM_2.5 data in 338 Chinese cities during 2014–2017, this study employed multi-temporal and multi-spatial scale statistical analysis to reveal the temporal and spatial characteristics of PM_2.5 patterns and a spatial econometric model to quantify the socio-economic driving factors of PM_2.5 concentration changes. The results are as follows: (1) The annual average value of PM_2.5 concentration decreased year by year and the monthly average showed a U-shaped curve from January to December. The daily mean value of PM_2.5 concentration had the characteristics of pulse-type fluctuation and the hourly variation presented a bimodal curve. (2) During 2014–2017, the overall PM_2.5 pollution reduced significantly, but that of more than two-thirds of cities still exceeded the standard value (35 μg/m³) regulated by Chinese government. PM_2.5 pollution patterns showed high values in central and eastern Chinese cities and low values in peripheral areas, with the distinction evident along the same line that delineates China’s uneven population distribution. (3) Population agglomeration, industrial development, foreign investment, transportation, and pollution emissions contributed to the increase of PM_2.5 concentration. Urban population density contributed most significantly while economic development and technological progress reduced PM_2.5 concentration. The results also suggest that China in general remains a “pollution shelter” for foreign-funded enterprises. Full article

Recent evidence suggests that many densely populated areas of the world will be uninhabitable in the coming century due to the depletion of resources, climate change, and increasing urbanization. This poses serious questions regarding the actions that require immediate attention, and opportunities to stave off massive losses of infrastructure, populations, and financial investments. The present study utilizes microclimate modeling to examine the role of landscape features as they affect ambient temperatures in one of the fastest growing regions of the world: Doha, Qatar. By modeling three study sites around Doha—one highly urbanized, one newly urbanizing, and one coastal low-density urbanized—the research indicates that at the neighborhood scale, the most effective scenario was that of adding mature trees along the sides of roads. In the coastal study area, the model results estimated a maximum hourly air temperature reduction of 1.35 °C, and in the highly urbanized inland site, surface temperature reductions were up to 15 °C at 12:00. While other scenarios were effective at reducing air and surface temperatures, the mean radiant temperature was also increased or nearly neutral for most of the other scenarios. This result highlights the need to develop improved shading measures for pedestrian pathways and outdoor recreational areas, especially for highly urbanized inland areas in Doha and cities with similar climatic conditions. Full article