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\"With over half of the world's population now living in urban areas, the ability to model and understand the structure and dynamics of cities is becoming increasingly valuable. Combining new data with tools and concepts from statistical physics and urban economics, this book presents a modern and interdisciplinary perspective on cities and urban systems. Both empirical observations and theoretical approaches are critically reviewed, with particular emphasis placed on derivations of classical models and results, along with analysis of their limits and validity. Key aspects of cities are thoroughly analyzed, including mobility patterns, the impact of multimodality, the coupling between different transportation modes, the evolution of infrastructure networks, spatial and social organisation, and interactions between cities. Drawing upon knowledge and methods from areas of mathematics, physics, economics and geography, the resulting quantitative description of cities will be of interest to all those studying and researching how to model these complex systems\"-- Provided by publisher.

In this comprehensive analysis of Chile’s air quality dynamics spanning 2016 to 2021, the utilization of data from the National Air Quality Information System (SINCA) and its network of monitoring stations was undertaken. Quintero, Puchuncaví, and Coyhaique were the focal points of this study, with the primary objective being the construction of predictive models for sulfur dioxide (SO 2 ), fine particulate matter (PM 2.5 ), and coarse particulate matter (PM 10 ). A hybrid forecasting strategy was employed, integrating Autoregressive Integrated Moving Average (ARIMA) models with Artificial Neural Networks (ANN), incorporating external covariates such as wind speed and direction to enhance prediction accuracy. Vital monitoring stations, including Quintero, Ventanas, Coyhaique I, and Coyhaique II, played a pivotal role in data collection and model development. Emphasis on industrial and residential zones highlighted the significance of discerning pollutant origins and the influence of wind direction on concentration measurements. Geographical and climatic factors, notably in Coyhaique, revealed a seasonal stagnation effect due to topography and low winter temperatures, contributing to heightened pollution levels. Model performance underwent meticulous evaluation, utilizing metrics such as the Akaike Information Criterion (AIC), Ljung-Box statistical tests, and diverse statistical indicators. The hybrid ARIMA-ANN models demonstrated strong predictive capabilities, boasting an R 2 exceeding 0.90. The outcomes underscored the imperative for tailored strategies in air quality management, recognizing the intricate interplay of environmental factors. Additionally, the adaptability and precision of neural network models were highlighted, showcasing the potential of advanced technologies in refining air quality forecasts. The findings reveal that geographical and climatic factors, especially in Coyhaique, contribute to elevated pollution levels due to seasonal stagnation and low winter temperatures. These results underscore the need for tailored air quality management strategies and highlight the potential of advanced modeling techniques to improve future air quality forecasts and deepen the understanding of environmental challenges in Chile.

Small towns play a crucial role in bridging urban and rural territory systems. While numerous studies have identified the characteristics and causes of small town shrinkage (STS), there remains an unexplored perspective on the reasons for their shrinkage from the perspective of the rural-urban relationship. To address this research gap, we investigated the relationship between STS and rural-urban interaction (RUI) in China. We hypothesized that a negative relationship existed between the degree of STS and the intensity of RUI. Using geo-statistical methods, such as the multi-scale geographical weighted regression (MGWR) model, the hypothesis was tested using Henan Province in China as a case study. The results indicated that the phenomenon of STS was observed extensively across the study region, with a 59% geographical overlap between the high-value area of STS and the low-value area of urban-rural interaction. Three distinct sub-types of STS regions were identified: shrinking regions along geographical borders, shrinking regions adjacent to metropolitan areas, and shrinking regions in ecologically fragile areas. The factors influencing STS demonstrated spatial heterogeneity and multi-scale characteristics. The findings will improve our understanding of urban shrinkage from a multi-level perspective and offer policy makers guidance for the sustainable development of small towns based on local conditions.

Coastal regions of China feature high population densities as well as wind-sensitive structures and are therefore vulnerable to tropical cyclones (TCs) with approximately six to eight landfalls annually. This study predicts TC wind hazard curves in terms of design wind speed versus return periods for major coastal cities of China to facilitate TC-wind-resistant design and disaster mitigation as well as insurance-related risk assessment. The 10 min wind information provided by the Japan Meteorological Agency (JMA) from 1977 to 2015 is employed to rebuild TC wind field parameters (radius of maximum winds Rmax,s and shape parameter of radial pressure profile Bs) at surface level using a height-resolving boundary layer model. These parameters will be documented to develop an improved JMA dataset. The probabilistic behaviors of historical tracks and wind field parameters at the first time step within a 500 km radius subregion centered at a site of interest are examined to determine preferable probability distribution models before stochastically generating correlated genesis parameters utilizing the Cholesky decomposition method. Recursive models are applied for translation speed, Rmax,s and Bs during the TC track and wind field simulations. Site-specific TC wind hazards are studied using 10 000-year Monte Carlo simulations and compared with code suggestions as well as other studies. The resulting estimated wind speeds for northern cities (Ningbo and Wenzhou) under a TC climate are higher than code recommendations, while those for southern cities (Zhanjiang and Haikou) are lower. Other cities show a satisfactory agreement with code provisions at the height of 10 m. Some potential reasons for these findings are discussed to emphasize the importance of independently developing hazard curves of TC winds.

Semi-volatile and intermediate-volatility organic compounds (S–IVOCs) are considered critical precursors of secondary organic aerosol (SOA), which is an important component of fine particulate matter (PM2.5). However, knowledge of the contributions of S–IVOCs to SOA is still lacking in the Pearl River Delta (PRD) region, southern China. Therefore, in this study, an emission inventory of S–IVOCs in the PRD region was developed for the first time for the year 2010. The S–IVOC emissions were calculated based on a parameterization method involving the emission factors of POA (primary organic aerosol), emission ratios of S–IVOCs to POA, and domestic activity data. The total emissions of S–IVOCs were estimated to be 323.4 Gg, with major emissions from central cities in the PRD, i.e., Guangzhou, Foshan, and Shenzhen. On-road mobile sources and industries were the two major contributors of S–IVOC emissions, with contributions of ∼42 % and ∼35 %, respectively. Furthermore, uncertainties of the emission inventory were evaluated through Monte Carlo simulation. The uncertainties ranged from −79 % to 229 %, which could be mainly attributed to mass fractions of OC (organic carbon) to PM2.5 from on-road mobile emissions and emission ratios of IVOCs ∕ POA. The developed S–IVOC emission inventory was further incorporated into the Weather Research and Forecasting with Chemistry (WRF-Chem) model with a volatility basis-set (VBS) approach to improve the performance of SOA simulation and to evaluate the influence of S–IVOCs on SOA formation at a receptor site (Wan Qing Sha (WQS) site) in the PRD. The following results could be obtained. (1) The model could resolve about 34 % on average of observed SOA concentrations at WQS after considering the emissions of S–IVOCs, and 18 %–77 % with the uncertainties of the S–IVOC emission inventory considered. (2) The simulated SOA over the PRD region was increased by 161 % with the input of S–IVOC emissions, and it could be decreased to 126 % after the reaction coefficient of S–IVOCs with OH radical was improved. (3) Among all anthropogenic sources of S–IVOCs, industrial emission was the most significant contributor of S–IVOCs for SOA formation, followed by on-road mobile, dust, biomass burning, residential, and off-road mobile emissions. Overall, this study firstly quantified emissions of S–IVOCs and evaluated their roles in SOA formation over the PRD, which contributes towards significantly improving SOA simulation and better understanding of SOA formation mechanisms in the PRD and other regions in China.

The acid deposition has been considered to be a severe environmental issue in China. The pH, electrical conductivity (EC), and concentrations of water soluble ions (NO3-, Cl−, Ca2+, K+, F−, NH4+, Mg2+, SO42-, and Na+) in the precipitation samples collected from 320 cities during 2011–2016 across China were measured. The mean concentrations of F−, NO3-, and SO42- were in the order of winter (6.10, 19.44, and 45.74 µeq L−1) > spring (3.45, 13.83, and 42.61 µeq L−1) > autumn (2.67, 9.73, and 28.85 µeq L−1) > summer (2.04, 7.66, and 19.26 µeq L−1). Secondary ions (SO42-, NO3-, and NH4+) and F− peaked in the Yangtze River Delta (YRD) and Sichuan basin (SB). Crustal ions (i.e. Ca2+, Mg2+), Na+, and Cl− showed the highest concentrations in the semi-arid regions and the coastal cities. The statistical methods confirmed that the mean anthropogenic contribution ratios to SO42-, F−, NO3-, and NH4+ at a national scale were 46.12 %, 71.02 %, 79.10 %, and 82.40 %, respectively. However, Mg2+ (70.51 %), K+ (77.44 %), and Ca2+ (82.17 %) mostly originated from the crustal source. Both Na+ (70.54 %) and Cl− (60.42 %) were closely linked to sea salt aerosols. On the basis of the stepwise regression (SR) analysis, it was proposed that most of the secondary ions and F− were closely related to gross industrial production (GIP), total energy consumption (TEC), vehicle ownership, and N fertilizer use, but the crustal ions (Ca2+ and K+) were mainly controlled by the dust events. The influence of dust days, air temperature, and wind speed on ions increased from southeast China (SEC) to central China, and then to northwest China (NWC), whereas the influence of socioeconomic factors on acid ions (SO42- and NO3-) displayed the higher value in east China.

Background The built environment is a structural determinant of health and has been shown to influence health expenditures, behaviors, and outcomes. Traditional methods of assessing built environment characteristics are time-consuming and difficult to combine or compare. Google Street View (GSV) images represent a large, publicly available data source that can be used to create indicators of characteristics of the physical environment with machine learning techniques. The aim of this study is to use GSV images to measure the association of built environment features with health-related behaviors and outcomes at the census tract level. Methods We used computer vision techniques to derive built environment indicators from approximately 31 million GSV images at 7.8 million intersections. Associations between derived indicators and health-related behaviors and outcomes on the census-tract level were assessed using multivariate regression models, controlling for demographic factors and socioeconomic position. Statistical significance was assessed at the α = 0.05 level. Results Single lane roads were associated with increased diabetes and obesity, while non-single-family home buildings were associated with decreased obesity, diabetes and inactivity. Street greenness was associated with decreased prevalence of physical and mental distress, as well as decreased binge drinking, but with increased obesity. Socioeconomic disadvantage was negatively associated with binge drinking prevalence and positively associated with all other health-related behaviors and outcomes. Conclusions Structural determinants of health such as the built environment can influence population health. Our study suggests that higher levels of urban development have mixed effects on health and adds further evidence that socioeconomic distress has adverse impacts on multiple physical and mental health outcomes.

National levels of personal health-care access and quality can be approximated by measuring mortality rates from causes that should not be fatal in the presence of effective medical care (ie, amenable mortality). Previous analyses of mortality amenable to health care only focused on high-income countries and faced several methodological challenges. In the present analysis, we use the highly standardised cause of death and risk factor estimates generated through the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) to improve and expand the quantification of personal health-care access and quality for 195 countries and territories from 1990 to 2015. We mapped the most widely used list of causes amenable to personal health care developed by Nolte and McKee to 32 GBD causes. We accounted for variations in cause of death certification and misclassifications through the extensive data standardisation processes and redistribution algorithms developed for GBD. To isolate the effects of personal health-care access and quality, we risk-standardised cause-specific mortality rates for each geography-year by removing the joint effects of local environmental and behavioural risks, and adding back the global levels of risk exposure as estimated for GBD 2015. We employed principal component analysis to create a single, interpretable summary measure–the Healthcare Quality and Access (HAQ) Index–on a scale of 0 to 100. The HAQ Index showed strong convergence validity as compared with other health-system indicators, including health expenditure per capita (r=0·88), an index of 11 universal health coverage interventions (r=0·83), and human resources for health per 1000 (r=0·77). We used free disposal hull analysis with bootstrapping to produce a frontier based on the relationship between the HAQ Index and the Socio-demographic Index (SDI), a measure of overall development consisting of income per capita, average years of education, and total fertility rates. This frontier allowed us to better quantify the maximum levels of personal health-care access and quality achieved across the development spectrum, and pinpoint geographies where gaps between observed and potential levels have narrowed or widened over time. Between 1990 and 2015, nearly all countries and territories saw their HAQ Index values improve; nonetheless, the difference between the highest and lowest observed HAQ Index was larger in 2015 than in 1990, ranging from 28·6 to 94·6. Of 195 geographies, 167 had statistically significant increases in HAQ Index levels since 1990, with South Korea, Turkey, Peru, China, and the Maldives recording among the largest gains by 2015. Performance on the HAQ Index and individual causes showed distinct patterns by region and level of development, yet substantial heterogeneities emerged for several causes, including cancers in highest-SDI countries; chronic kidney disease, diabetes, diarrhoeal diseases, and lower respiratory infections among middle-SDI countries; and measles and tetanus among lowest-SDI countries. While the global HAQ Index average rose from 40·7 (95% uncertainty interval, 39·0–42·8) in 1990 to 53·7 (52·2–55·4) in 2015, far less progress occurred in narrowing the gap between observed HAQ Index values and maximum levels achieved; at the global level, the difference between the observed and frontier HAQ Index only decreased from 21·2 in 1990 to 20·1 in 2015. If every country and territory had achieved the highest observed HAQ Index by their corresponding level of SDI, the global average would have been 73·8 in 2015. Several countries, particularly in eastern and western sub-Saharan Africa, reached HAQ Index values similar to or beyond their development levels, whereas others, namely in southern sub-Saharan Africa, the Middle East, and south Asia, lagged behind what geographies of similar development attained between 1990 and 2015. This novel extension of the GBD Study shows the untapped potential for personal health-care access and quality improvement across the development spectrum. Amid substantive advances in personal health care at the national level, heterogeneous patterns for individual causes in given countries or territories suggest that few places have consistently achieved optimal health-care access and quality across health-system functions and therapeutic areas. This is especially evident in middle-SDI countries, many of which have recently undergone or are currently experiencing epidemiological transitions. The HAQ Index, if paired with other measures of health-system characteristics such as intervention coverage, could provide a robust avenue for tracking progress on universal health coverage and identifying local priorities for strengthening personal health-care quality and access throughout the world. Bill & Melinda Gates Foundation.

Crime is a major threat to society's well-being but lacks a statistical characterization that could lead to uncovering some of its underlying mechanisms. Evidence of nonlinear scaling of urban indicators in cities, such as wages and serious crime, has motivated the understanding of cities as complex systems-a perspective that offers insights into resources limits and sustainability, but that usually neglects details of the indicators themselves. Notably, since the nineteenth century, criminal activities have been known to occur unevenly within a city; crime concentrates in such way that most of the offenses take place in few regions of the city. Though confirmed by different studies, this concentration lacks broad analyses on its characteristics, which hinders not only the comprehension of crime dynamics but also the proposal of sounding counter-measures. Here, we developed a framework to characterize crime concentration which divides cities into regions with the same population size. We used disaggregated criminal data from 25 locations in the U.S. and the U.K., spanning from 2 to 15 years of longitudinal data. Our results confirmed that crime concentrates regardless of city and revealed that the level of concentration does not scale with city size. We found that the distribution of crime in a city can be approximated by a power-law distribution with exponent α that depends on the type of crime. In particular, our results showed that thefts tend to concentrate more than robberies, and robberies more than burglaries. Though criminal activities present regularities of concentration, we found that criminal ranks have the tendency to change continuously over time-features that support the perspective of crime as a complex system and demand analyses and evolving urban policies covering the city as a whole.