Search

Previous allocations of new urban land were ineffective because they lacked synergy between quota and space, challenging the government planning authority. This study proposes a new and more reasonable urban land allocation method to guide the smart growth of cities. We used a logistic regression model and multisource data to explore the laws of urban growth and employed a cellular automata (CA) model to simulate this under inertial and constrained scenarios. In addition, the disparities between both scenarios concerning allocation were analyzed. We realized the synergy of quota and space allocations of new urban land through urban growth simulation. Further, the allocation of new urban land was more consistent with the development strategy of Guangzhou under a constrained scenario. The allocation of space was more regular and concentrated under a constrained scenario, which aligns with the requirements of the Government Land Space Planning. Additionally, in the constrained scenario, the bottom lines of cultivated land protection, ecological service, and geological safety were better controlled. This study compensated for the shortcomings of the disjoined quota and space allocations of new urban land and proved that a constrained scenario can more effectively promote reasonable urban growth. [ABSTRACT FROM AUTHOR]

The recognition of urban functional areas (UFAs) is of great significance for the understanding of urban structures and urban planning. Due to the limitation of data sources, early research was characterized by problems such as singular data, incomplete results, and inadequate consideration of the socioeconomic environment. The development of multi-source big data brings new opportunities for dynamic recognition of UFAs. In this study, a sub-block function recognition framework that integrates multi-feature information from building footprints, point-of-interest (POI) data, and Landsat images is proposed to classify UFAs at the sub-block level using a random forest model. The recognition accuracies of single- and mixed-function areas in the core urban area of Guangzhou, China, obtained by this framework are found to be significantly higher than those of other methods. The overall accuracy (OA) of single-function areas is 82%, which is 8–36% higher than that of other models. The research conclusions show that the introduction of the three-dimensional (3D) features of buildings and finer land cover features can improve the recognition accuracy of UFAs. The proposed method that uses open access data and achieves comprehensive results provides a more practical solution for the recognition of UFAs. [ABSTRACT FROM AUTHOR]

As a common vector-borne disease, dengue fever remains challenging to predict due to large variations in epidemic size across seasons driven by a number of factors including population susceptibility, mosquito density, meteorological conditions, geographical factors, and human mobility. An ensemble forecast system for dengue fever is first proposed that addresses the difficulty of predicting outbreaks with drastically different scales. The ensemble forecast system based on a susceptible-infected-recovered (SIR) type of compartmental model coupled with a data assimilation method called the ensemble adjusted Kalman filter (EAKF) is constructed to generate real-time forecasts of dengue fever spread dynamics. The model was informed by meteorological and mosquito density information to depict the transmission of dengue virus among human and mosquito populations, and generate predictions. To account for the dramatic variations of outbreak size in different seasons, the effective population size parameter that is sequentially updated to adjust the predicted outbreak scale is introduced into the model. Before optimizing the transmission model, we update the effective population size using the most recent observations and historical records so that the predicted outbreak size is dynamically adjusted. In the retrospective forecast of dengue outbreaks in Guangzhou, China during the 2011–2017 seasons, the proposed forecast model generates accurate projections of peak timing, peak intensity, and total incidence, outperforming a generalized additive model approach. The ensemble forecast system can be operated in real-time and inform control planning to reduce the burden of dengue fever. Author summary: Dengue fever is one of the key neglected tropical diseases (NTDs) and threatens the health of billions of people worldwide. Nevertheless, accurate forecasts of dengue outbreaks remain difficult: the intensity of dengue fever outbreaks varies remarkably among different seasons and there are generally no cases or only sporadic cases of dengue fever outside the outbreak season. From the perspective of data distribution characteristics, the time series of dengue incidence are following zero-inflated distributions. Therefore, generating model forecasts using the type of zero-inflated data is usually beyond the predictive power of general discrete distributions such as Poisson distributions. These characteristics impose great challenges in accurate dengue predictions. We proposed to establish a combined forecast system based on susceptible-infected-recovered (SIR) model coupled with ensemble adjusted Kalman filter (EAKF) algorithm for depicting dengue transmission dynamics, thus accounting for the dramatic variations of outbreak size in different seasons. This approach allows for early warning of epidemics and outbreaks of dengue, and can inform control planning to reduce the burden of the disease. [ABSTRACT FROM AUTHOR]

Some studies have demonstrated that precipitation is an important risk factor of dengue epidemics. However, current studies mostly focused on a single precipitation variable, and few studies focused on the impact of precipitation patterns on dengue epidemics. This study aims to explore optimal precipitation patterns for dengue epidemics. Weekly dengue case counts and meteorological data from 2006 to 2018 in Guangzhou of China were collected. A generalized additive model with Poisson distribution was used to investigate the association between precipitation patterns and dengue. Precipitation patterns were defined as the combinations of three weekly precipitation variables: accumulative precipitation (Pre_A), the number of days with light or moderate precipitation (Pre_LMD), and the coefficient of precipitation variation (Pre_CV). We explored to identify optimal precipitation patterns for dengue epidemics. With a lead time of 10 weeks, minimum temperature, relative humidity, Pre_A, and Pre_LMD were positively associated with dengue, while Pre_CV was negatively associated with dengue. A precipitation pattern with Pre_A of 20.67–55.50 mm per week, Pre_LMD of 3–4 days per week, and Pre_CV less than 1.41 per week might be an optimal precipitation pattern for dengue epidemics in Guangzhou. The finding may be used for climate-smart early warning and decision-making of dengue prevention and control. [ABSTRACT FROM AUTHOR]

Background: The coronavirus disease 2019 (COVID-19) pandemic has been reported to have negative psychological impact on mental health. Nonetheless, there are few studies investigating the impacts on pregnant women. This study investigated the psychological impact of COVID-19 pandemic on pregnant women, and the associated risk factors that moderated this impact. Methods and Materials: A total of 2,798 pregnant participants were recruited from the Guangzhou Women and Children's Medical Center. The Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7) and Insomnia Severity Index (ISI) were used to assess depression, generalized anxiety disorder and insomnia, respectively, during and before the COVID-19 pandemic. The Impact of Event Scale-Revised (IES-R) was used to assess psychological stress during the COVID-19 pandemic. Results: During the COVID-19 pandemic, over one third of pregnant participants reported mild depression, around 20% experienced mild generalized anxiety, about one third reported problems with sleeping, and more than 15% felt mild psychological stress. The occurrence of psychological problems was significantly higher during the COVID-19 pandemic when compared to before the outbreak. The previously described pattern that pregnant women in the first trimester are more likely to report depression, and those in the third trimester are more likely to report insomnia and psychological stress, was also recognized in our study population. Mental health issues existing before the outbreak were risk factors, while family support was a protective factor in the occurrence of the measured mental health problems during the COVID-19 pandemic. Conclusion: Our data suggest pregnant women's mental health is inevitably affected during the COVID-19 pandemic. Pregnant women in the first and third trimester and those who experienced mental issues before the outbreak may be particularly affected. [ABSTRACT FROM AUTHOR]

Background: Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data. Methodology and principal findings: A Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29). Conclusions: Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou. [ABSTRACT FROM AUTHOR]

The current air quality indices have been criticized for not capturing combined health effects of multiple air pollutants. We proposed an Air Quality Health Index (AQHI) based on the air pollution-mortality associations for communicating health risks of air pollution. Time-series studies were conducted to estimate the associations between air pollutants including sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), ozone (O 3 ), suspended particulate matter smaller than 2.5 μm in aerodynamic diameter (PM 2.5 ) and mortality in Guangzhou from 2012 to 2015. The sum of excess mortality risk was calculated to construct the AQHI, which was then adjusted to an arbitrary scale. We then assessed the validity of AQHI. An interquartile increase (IQR) of AQHI was associated with 3.61% [95% confidence interval (95% CI), 2.85%–4.37%], 3.73% (95%CI, 2.18%–5.27%) and 4.19% (95%CI, 2.87%–5.52%) increase of mortality, respiratory and cardiovascular hospital admissions, respectively. Compared with the currently used Air Quality Index (AQI), AQHI had higher effects on mortality and morbidity. Our study suggests that AQHI might comprehensively capture the combined effects of air pollution, which make it be a more valid communication tool of air pollution-related health risk. [ABSTRACT FROM AUTHOR]

Though rainfall is recognized as one of the main mechanisms to reduce atmospheric particulate pollution, few studies have quantified this effect, particularly the corresponding lag effect and threshold. This study aimed to investigate the association between rainfall and air quality using a distributed lag non-linear model. Daily data on ambient PM 2.5 and PM 2.5–10 (particulate matter with an aerodynamic diameter less than 2.5 μm and from 2.5 to 10 μm) and meteorological factors were collected in Guangzhou and Xi'an from 2013 to 2014. A better washout effect was found for PM 2.5–10 than for PM 2.5 , and the rainfall thresholds for both particle fractions were 7 mm in Guangzhou and 1 mm in Xi'an. The decrease in PM 2.5 levels following rain lasted for 3 and 6 days in Guangzhou and Xi'an, respectively. Rainfall had a better washout effect in Xi'an compared with that in Guangzhou. Findings from this study contribute to a better understanding of the washout effects of rainfall on particulate pollution, which may help to understand the category and sustainability of dust-haze and enforce anthropogenic control measures in time. [ABSTRACT FROM AUTHOR]

In 2012, Chinese Environmental Bureau modified its National Ambient Air Quality Standards to include fine particulate matter (PM 2.5 ). Recent air pollution monitoring data shows that numerous locations have exceeded this standard, which may have resulted in avoidable adverse health effects. For example, among the 74 Chinese cities with PM 2.5 monitoring data in 2013, only three cities attained the annual air quality standard (35 μg/m 3 ). This study aimed to quantify the potential short- and long-term health benefits from achieving the Chinese ambient air quality standard and WHO’s air quality objectives. A generalized additive model was used to estimate the short-term association of mortality with changes in daily PM 2.5 concentrations, based on which we estimated the potential premature mortality reduction that would have been achieved during the period of 2012–2015 if the daily air quality standard had been met in Guangzhou, China; we also estimated the avoidable deaths if attaining the annual air quality standard using the relative risk obtained from a previous cohort study. During the study period, there were 160 days exceeding the national daily PM 2.5 standard (75 μg/m 3 ) in Guangzhou, and the annual average concentration (47.7 μg/m 3 ) was higher than the air quality standard of 35 μg/m 3 . Significant associations between PM 2.5 and mortality were observed. An increase of 10 μg/m 3 in PM 2.5 was associated with increases in daily death counts of 0.95% (95% CI: 0.56%, 1.34%) in natural mortality, 1.31% (95% CI: 0.75%, 1.87%) in cardiovascular mortality, and 1.06% (95% CI: 0.19%, 1.94%) in respiratory mortality. The health benefits of attaining the national daily air quality standard of PM 2.5 (75 μg/m 3 ) would have prevented 143 [95% confidence interval (CI): 84, 203] fewer natural deaths, including 84 (95% CI: 48, 121) fewer cardiovascular deaths and 27 (95% CI: 5, 49) fewer respiratory deaths. Had the annual PM 2.5 levels been reduced to 35 μg/m 3 , an estimated 3875 (95% CI: 1852, 6074) natural deaths, 2378 (95% CI: 800, 4230) cardiovascular deaths, and 227 (95% CI: -437, 1033) respiratory deaths could have been prevented. Even greater substantial mortality reductions could be achieved if the WHO’s air quality objectives were met. Our study suggests that air pollution is significantly associated with mortality in Guangzhou, and more stringent air quality standards would significantly reduce air pollution-related premature mortality. [ABSTRACT FROM AUTHOR]

Though significant associations between particulate matter (PM) air pollution and cardiovascular diseases have been widely reported, it remains unclear what characteristics, such as particle size and chemical constituents, may be responsible for the effects. A time-series model was applied to examine the cardiovascular effects of particle size (for the period of 2009–2011) and chemical constituents (2007–2010) in Guangzhou, we controlled for potential confounders in the model, such as time trends, day of the week, public holidays, meteorological factors and influenza epidemic. We found significant associations of cardiovascular mortality with PM 10 , PM 2.5 and PM 1 ; the excess risk (ER) was 6.10% (95% CI: 1.76%, 10.64%), 6.11% (95% CI: 1.76%, 10.64%) and 6.48% (95% CI: 2.10%, 11.06%) for per IQR increase in PM 10 , PM 2.5 and PM 1 at moving averages for the current day and the previous 3 days (lag 03 ), respectively. We did not find significant effects of PM 2.5-10 and PM 1-2.5 . For PM 2.5 constituents, we found that organic carbon, elemental carbon, sulfate, nitrate and ammonium were significantly associated with cardiovascular mortality, the corresponding ER for an IQR concentration increase at lag 03 was 1.13% (95% CI: 0.10%, 2.17%), 2.77% (95% CI: 0.72%, 4.86%), 2.21% (95% CI: 1.05%, 3.38%), 1.98% (95% CI: 0.54%, 3.44%), and 3.38% (95% CI: 1.56%, 5.23%), respectively. These results were robust to adjustment of other air pollutants and they remained consistent in various sensitivity analyses by changing model parameters. Our study suggests that PM 1 and constituents from combustion and secondary aerosols might be important characteristics of PM pollution associated with cardiovascular mortality in Guangzhou. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the effects of dust–haze on mortality and to estimate the seasonal and individual-specific modification effects in Guangzhou, China. Mortality, air pollution and meteorological data were collected for 2006–2011. A dust–haze day was defined as daily visibility <10 km with relative humidity <90%. This definition was further divided into light (8–10 km), medium (5–8 km) and heavy dust–haze (<5 km). A distributed lag linear model (DLM) was employed. Light, medium and heavy dust–haze days were associated with increased mortality of 3.4%, 6.8% and 10.4% respectively, at a lag of 0–6 days. This effect was more pronounced during the cold season, for cardiovascular mortality (CVD), respiratory mortality (RESP), in males and people ≥60years. These effects became insignificant after adjustment for PM10. We concluded that dust–haze significantly increased mortality risk in Guangzhou, China, and this effect appears to be dominated by particulate mass and modified by season and individual-specific factors. [Copyright &y& Elsevier]

Abstract: Effects of ozone on mortality have been widely assessed in developed countries but rarely in developing countries, and the effects of season and temperature on these associations remain unclear. The present study aimed to explore the modifying effects of temperature on the association of ozone with mortality, and to examine the lag effect structure in Guangzhou, China. Daily non-accidental mortality, air pollution and meteorological data from January 1st 2006 to December 31st 2008 in Guangzhou were collected. Generalized additive models (GAM) and distributed lag models (DLM) were used to estimate the excess risk (ER) of ozone on daily mortality and capture the lag effect structure. Results revealed that, in cold season, an increment in the ozone concentration of 10 μg m− 3 was associated with a 0.87% (95% CI: −0.06 to 2.29%) and 3.34% (95%CI: 1.36–5.35%) increase of mortality for lag0 and lag0–6, respectively. However, no significant effects were observed in the warm season. Daily average temperature and ozone concentration had interactive negative effects on non-accidental mortality in the cold season and at daily average temperatures in the 0–25th percentile, and mortality displacement was also found in these conditions. Therefore, the short-term effect of ambient ozone on mortality is modified by temperature in Guangzhou, China. [Copyright &y& Elsevier]

Short-term exposure to fine particulate matter (PM 2.5) increases the excess risk (ER) of deaths. The metals are important constituents that are responsible for health risks of PM 2.5. However, the short-term mortality risks that are associated with individual PM 2.5 -bound metals are not comprehensively understood. Estimate the short-term mortality risks and corresponding thresholds of PM 2.5 -bound metals, with the purpose of advancing our understanding of the health risks of PM 2.5 exposure, meanwhile, guiding the policy making of PM 2.5 pollution prevention and early warning. Daily PM 2.5 samples, which were collected in the urban region of Guangzhou from 2015 to 2016, were analyzed for metals. Air pollutants, meteorological and mortality data were collected during the same period. A distributed lag non-linear model was used to associate the ER of mortality with individual PM 2.5 -bound metals. A cluster analysis was processed to classify the cumulative ER of different metals into groups. 11 PM 2.5 -bound metals were statistically significantly associated with non-accidental death; the highest cumulative ER of mortality was 2.22%, 1.85%, 1.42%, 2.04%, 1.41%, 1.26%, 0.65%, 2.24%, 1.23%, 2.86%, and 1.84% for an interquartile range (IQR) increase in concentrations of lead (Pb), cadmium (Cd), arsenic (As), selenium (Se), antimony (Sb), manganese (Mn), nickel (Ni), thallium (Tl), barium (Ba), aluminum (Al), and iron (Fe), respectively. The metals were classified into high (Pb, Cd, Se, Tl, and Al), middle (As, Sb, Mn, Ba, and Fe), and low (Ni, copper, and zinc) ER groups according to their highest cumulative ER values. No threshold was found for concentration-response relationships between all the metals and mortality. Pb, Cd, Se, Tl, and Al had more contributions to PM 2.5 -related mortality risk than the other metals. Specific emission source-control measures were needed to reduce the mortality risk of PM 2.5 exposure. The mortality risks, which were associated with low-level exposure of PM 2.5 -bound metals, deserve attention in the design of early warning and self-prevention. Comprehensive exposure assessment of PM 2.5 -bound metals is required to understand and reduce the PM 2.5 -related health risks. [Display omitted] • Daily PM 2.5 samples were collected for two years and analyzed for metals. • Daily mortality was significantly associated with 11 PM 2.5 -bound metals. • Metals were classified into three groups according to their ER of mortality. • Pb, Cd, Se, Tl, and Al contributed more to mortality risk of PM 2.5 than other metals. • Mortaltiy risks caused by low-level exposure of metals cannot be neglected. [ABSTRACT FROM AUTHOR]

Background: Dengue is becoming a major public health concern in Guangdong (GD) Province of China. The problem was highlighted in 2014 by an unprecedented explosive outbreak, where the number of cases was larger than the total cases in previous 30 years. The present study aimed to clarify the spatial and temporal patterns of this dengue outbreak.Methods: Based on the district/county-level epidemiological, demographic and geographic data, we first used Moran's I statistics and Spatial scan method to uncover spatial autocorrelation and clustering of dengue incidence, and then estimated the spatial distributions of mosquito ovitrap index (MOI) by using inverse distance weighting. We finally employed a multivariate time series model to quantitatively decompose dengue cases into endemic, autoregressive and spatiotemporal components.Results: The results indicated that dengue incidence was highly spatial-autocorrelated with the inclination of clustering and nonuniformity. About 12 dengue clusters were discovered around Guangzhou and Foshan with significant differences by district/county, where the most likely cluster with the largest relative risk located in central Guangzhou in October. Three significant high-MOI areas were observed around Shaoguan, Qingyuan, Shanwei and Guangzhou. It was further found the districts in Guagnzhou and Foshan were prone to local autoregressive transmission, and most region in southern and central GD exhibited higher endemic components. Moreover, nearly all of districts/counties (especially the urban area) have cases that were infected in adjacent regions.Conclusions: The study can help to clarify the heterogeneity and the associations of dengue transmission in space and time, and thus provide useful information for public health authorities to plan dengue control strategies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Alkalization-Fenton-Chlorination was proposed to treat electroplating wastewater. • First adding NaOH to pH of 3 produced high-purity Fe(PO 3) 3 precipitates (98.4%). • Second adding Ca(OH) 2 to pH of 9 achieved high pollutants removal (metals, F, P). • Fenton parameters (pH, H 2 O 2 , Fe2+, time) were optimized for COD and Ni removal. • Chlorination parameters (NaClO dosage, time) were optimized for NH 3 -N removal. Electroplating wastewater rich in metal ions and acids has dual attributes of real environmental pollutant and potential resource carrier. Targeting resource recovery and up-to-standard discharge from electroplating wastewater treatment in this study, a hybrid process of stepwise alkalization, Fenton, and chlorination was proposed to treat real electroplating wastewater from Guangzhou, China, where pH was 1.1 and Ag, Fe, Ni, Cu, Zn, Cr, P, F, chemical oxygen demand (COD), NH 3 -N was 624.9, 11266.9, 914.7, 1169.6, 13.8, 1971, 8750.8, 7433.1, 382.3, 451.3 mg/L, respectively. Pollutants removal from liquid phase and precipitates composition were systematically investigated during stepwise alkalization via pH control, showing that first adding NaOH to pH of 3 produced high-purity (98.4 %) Fe(PO 3) 3 precipitates suitable for high value-added resource recovery, second adding Ca(OH) 2 to pH of 9 achieved high pollutants removal (especially F and P) and cost savings, and centrifugation with 2000 g and 10 min was appropriate for precipitates separation. Aiming at COD and chelated/complexed Ni removal from treated electroplating wastewater by stepwise alkalization, the optimal parameters for Fenton process in this study were determined as the initial pH of 3, mass ratio of H 2 O 2 to COD of 2.0, molar ratio of Fe2+ to H 2 O 2 of 1.11, and reaction time of 2 h, under which all pollutants except NH 3 -N met the discharge standards in Guangdong Province, China (DB 44/1597-2015), where the maximum concentration of Ag, Fe, Ni, Cu, Zn, Cr, P, F, COD, NH 3 -N was set as 0.1, 2, 0.5, 0.5, 1, 0.5, 1, 10, 80, 15 mg/L, respectively. Further chlorination treatment via adding NaClO for NH 3 -N removal showed that the optimal molar ratio of NaClO to NH 3 -N was 2.89 and the optimal reaction time was 90 min in this study, under which NH 3 -N removal followed first-order reaction kinetics with rate constant of 0.01831 min−1, NH 3 -N decreased to 12.6 mg/L satisfying the discharge standard, and COD was simultaneously reduced to 26.7 mg/L. These findings demonstrate the proposed hybrid process of stepwise alkalization, Fenton, and chlorination is promising for electroplating wastewater treatment and resource recovery. [ABSTRACT FROM AUTHOR]

A few studies have linked ambient air pollution with sex ratio at birth. Most of these studies examined the long-term effects using spatial or temporal comparison approaches. This study aimed to investigate whether parental exposure to air pollution before conception date could affect the likelihood of the offspring being male or female. We used the information collected in a major maternal hospital in Guangzhou, China. The parental exposure to air pollution was assessed using the air pollution concentration before the conception date. Logistic regression models were used to assess the association between air pollution exposure and birth sex with adjustment for potential confounding factors, such as maternal age, parental education levels, long-term trend, season, and weather condition (mean temperature and relative humidity). The analysis revealed that higher air pollution was associated with higher probability of female newborns, with the effective exposure around one week prior to conception date. In the one-pollutant models, PM 10 , SO 2 and NO 2 had significant effects. For example, the excess risk was 0.61% (95% confidence interval (95% CI): 0.36%, 0.86%) for a 10 ug/m 3 increase in lag 2 day's PM 10 , 0.42% (95% CI: 0.21%, 0.64%) for lag 3 day's SO 2 and 0.97% (95% CI: 0.44%, 1.50%) for lag 3 day's NO 2 ; and in two-pollutant models, PM 10 remained statistically significant. These results suggest that parental exposure to ambient air pollution a few days prior to conception might be a contributing factor to higher probability of giving birth to female offspring in Guangzhou. [ABSTRACT FROM AUTHOR]

Prior studies that examined the association between temperature and mortality relied on mean temperature, maximum temperature, minimum temperature, humidex, and daily temperature variability, not accounting for variations in hourly temperature throughout the day. We proposed an indicator, excess degree-hours, to examine the association between temperature and mortality. A distributed lag non-linear model (DLNM) was used to determine the hot (27.8 °C) and cold (24.3 °C) threshold. Hourly temperature in Guangzhou, China were summarized with extreme heat expressed as sum of degree-hours >27.8 °C and extreme cold as sum of degree-hours <24.3 °C within one day from January 1, 2012 to December 31, 2015. We then estimated the associations of daily mortality with hot and cold degree-hours in both hot and cold season. We also calculated the mortality burden of excess degree-hours. An interquartile range (IQR) increase of hot degree-hours was associated with 2.11% (95% confidence interval [95% CI]: 1.25%, 2.98%), 3.74% (95% CI: 0.71%, 6.86%), and 2.63% (95% CI: 0.70%, 4.59%) increments in non-injury related death, respiratory mortality, and cardiovascular mortality, respectively. While the corresponding excess risk for an IQR increase of cold degree-hours was 2.42% (95% CI: 1.97%, 2.88%), 3.16% (95% CI: 2.57%, 3.76%), and 2.93% (95% CI: 1.98%, 3.88%). The estimated mortality burdens for hot and cold degree-hours were 1366,2465, respectively. The excess degree-hours reduced to a single indication in duration and intensity is an approach and shows a different perspective and significant extreme weather effects on human health. • Excess degree-hours were used to examine the effect on mortality. • Daily temperature may underrate or overlook the effects of hourly temperatures. • The excess degree-hours shows extreme weather effects on human health. [ABSTRACT FROM AUTHOR]

Ozone (O 3) exposure may lead to the development and exacerbation of asthma or wheezing in postnatal children; however, it has rarely been studied before and during pregnancy. Wheezing is one of the most common symptoms when diagnosing of asthma; thus, we investigated the associations of O 3 exposure before and during pregnancy with wheezing in preschool children and the potential susceptible exposure windows from a heavily polluted city in China. This population-based birth cohort study, which included 3725 mother-child pairs from Guangzhou, began in 2016, and the follow-up period ended on July 31, 2020. We used a spatiotemporal land-use-regression model combined with activity patterns to estimate the daily O 3 exposure levels during the pre-pregnancy period and each trimester, and wheezing was recorded by reviewing medical records. We used the Cox proportional hazard model to quantify the effects of O 3 exposure on childhood wheezing adjusted for potential confounders. No significant association was detected between pre-pregnancy exposure to O 3 and childhood wheezing. However, increased ambient O 3 exposures throughout pregnancy and in the second trimester were positively associated with the risk of childhood wheezing, with hazard ratios (HRs) and 95% confident intervals (CIs) per interquartile range (IQR) increment of 1.22 (95% CI: 1.04–1.44) and 1.31 (95% CI: 1.09–1.58), respectively. The effects of maternal O 3 exposure on childhood wheezing risk was stronger when the exposure occurred in the warm conception season (P < 0.05). Significant childhood wheezing risk could be attributable to maternal O 3 exposure, especially during the second trimester and with warm-season conception in Guangzhou. Further cohorts of children, particularly school age children who have more robust asthma diagnoses, should be investigated in the future. • O 3 exposures during the whole pregnancy may increase the risk of childhood wheezing. • The second trimester may be the susceptible exposure window of O 3 effect • Conception season may be the effect modifier for O 3 leading to childhood wheezing. • The effect of O 3 exposures during pre-pregnancy is still inconclusive. [ABSTRACT FROM AUTHOR]

Heavy metals play an important role in inducing fine particulate matter (PM 2.5) related cardiovascular disease (CVD). However, most of the past researches concerned the associations between CVD mortality and the PM 2.5 mass, which may not reveal the CVD mortality risk contributed by heavy metals in PM 2.5. This study explored the correlations between individual heavy metals in PM 2.5 and CVD mortality, identified the heavy metals that significantly contribute to PM 2.5 -related CVD, heart disease (HD), and cerebrovascular disease (CEV) mortality, and attempted to establish corresponding source control measures. Over a 2-year study period, PM 2.5 was sampled daily in Guangzhou, China and analyzed for heavy metals. The airborne pollution and weather data, along with CVD, HD, and CEV mortality, were obtained at the same time. The excess risk (ER) of mortality was linked to the individual heavy metals using a distributed lag non-linear model. PM 2.5 and most heavy metals showed significant correlations with the CVD, HD, and CEV mortality; the largest cumulative ER (LCER) values of CVD mortality associated with an interquartile range increase in the levels of lead, cadmium, arsenic, selenium, antimony, nickel, thallium, aluminum, iron, and PM 2.5 were 2.43%, 2.23%, 1.66%, 2.39%, 1.19%, 1.21%, 2.69%, 3.29%, 1.74%, and 2.40%, respectively. Most heavy metals showed comparable LCER values of HD and CEV mortality. Heavy metals with the addition of PM 2.5 were divided into three groups following their LCER values; lead, cadmium, arsenic, antimony, thallium, zinc, aluminum, and iron, whose contributions were greater than or equal to the average effect of the PM 2.5 components, should be limited on a priority basis. These findings indicated that heavy metals play roles in the CVD, HD, and CEV mortality risk of PM 2.5 , and specific control measures which aimed at the emission sources should be taken to reduce the CVD mortality risk of PM 2.5. [Display omitted] • Daily levels of heavy metals in PM 2.5 were analyzed from a 2-year sampling. • Nine heavy metals in PM 2.5 were significantly associated with CVD mortality. • Precise source control of Pb, Cd, Se, Sb, Tl, Zn, Al, and Fe should be conducted. [ABSTRACT FROM AUTHOR]

Cellular automata (CA) have been increasingly used to simulate complex urban systems. Empirical data can be used to calibrate CA models so that realistic urban patterns can be generated. Traditionally, the calibration procedure employs linear regression methods, e.g., multicriteria evaluation. However, the evolution of urban systems often manifests the complexity of non-linear features, for which the linear transition rules are insufficient. This paper proposes to use the kernel-based learning techniques to acquire non-linear transition rules for CA. The kernel-based approach maps the original data vectors to an implicit high-dimensional feature space, through which complex non-linear problems are translated into simple linear problems. Compared with a widely used non-linear method, neural network, the kernel method is more mathematically “transparent” and therefore the results are easier to be analyzed. A case study of simulating the expansion of Guangzhou, a fast growing city in China, shows that the kernel-based CA even achieves a slightly higher accuracy than a neural-network-based CA. [Copyright &y& Elsevier]

The frequency and intensity of compound hot extremes will be likely to increase in the context of global warming. Epidemiological studies have demonstrated the adverse effect of simple hot extreme events on mortality, but little is known about the effects of compound hot extremes on mortality. Daily meteorological, demographic, and mortality data during 2011–2017 were collected from 160 streets in Guangzhou City, China. We used distributed lag non-linear model (DLNM) to analyze the associations of different hot extremes with mortality risk in each street. Street-specific associations were then combined using a meta-analysis approach. To assess the spatial distribution of vulnerability to compound hot extremes, vulnerable characteristics at street level were selected using random forest model, and then we calculated and mapped spatial vulnerability index (SVI) at each street in Guangzhou. At street level, compared with normal day, compound hot extreme significantly increased mortality risk (relative risk(RR)=1.43, 95%CI:1.28–1.59) with higher risk for female (RR=1.54 [1.35–1.76]) and the elderly(RR for aged 65–74=1.41 [1.14–1.74]; RR for ≥75years=1.63 [1.45–1.84]) than male (RR=1.32 [1.15–1.52]) and population <65 years (RR=1.01 [0.83–1.22]). Areas with high vulnerability were in the urban center and the edge of suburban. High proportion of population over 64 years old in urban center, and high proportions of outdoor workers and population with illiteracy in suburban areas were the determinants of spatial vulnerability. We found that compound hot extreme significantly increased mortality risk at street level, which is modified by socio-economic and demographic factors. Our findings help allocate resources targeting vulnerable areas at fine-spatial scale.° • Compound hot extreme increased 43% mortality compared to normal day with high vulnerability for the elderly and female. • Areas with high vulnerability were in the urban center and the edge of suburban. • High proportion of the elderly or outdoor workers or population with illiteracy contribute to spatial vulnerability. [ABSTRACT FROM AUTHOR]

Although studies have assessed the associations between prenatal exposure with fine particulate matter (PM) with birth weight, few have investigated the effect of PM 1 exposure and identified the susceptible exposure window. Additionally, a baby boom occurred after China implemented the universal two-child policy, but whether the effects of PM are modified by birth order remains unknown. The objective of this study was to estimate the effects of prenatal exposure to PM (PM 2.5 and PM 1) on birth weight, identify the susceptible exposure windows, and assess the modifying effect of birth order on the effects of PM exposure. All participants were selected from the Prenatal Environment and Offspring Health (PEOH) cohort conducted since 2016 in Guangzhou, China. A spatiotemporal land–use-regression (STLUR) model was used to estimate a pregnant woman's weekly PM 2.5 exposure, and a generalized additive model (GAM) was used to estimate each PM 1 exposure. A distributed lag non-linear model (DLNM) was applied to assess the exposure–lag-response associations between weekly PM exposure and birth weight. The sample included 4086 pregnant women. The results showed that maternal exposure to PM 2.5 and PM 1 during the 15th to 24th and 16th to 24th gestational weeks were associated with lower birth weight, the strongest association was observed in the 19th week, during which each 10 μg/m3 increment in PM 2.5 and PM 1 was associated with a 1.47 g (95% CI: 2.49 g, 0.44 g) and 1.58 g (95%CI: 2.74 g, 0.41 g) decrease in birth weight, respectively. Compared with the first-born neonates, greater effects of PM 2.5 and PM 1 exposure on birth weight were observed among the second-born neonates in the 15th to 21st and 15 h to 22nd gestational weeks. Further studies tracking the health of the second-born children are warranted because they may be more sensitive to environmental factors than the first-born children. • First to study the modifying effects of birth order on PM exposures and birthweight. • Prenatal exposure to higher PM 2.5 and PM 1 were associated with birth weight loss. • PM 1 may have a stronger effect on birthweight than PM 2.5. • The 15th to 24th and 16th to 24th gestational weeks might be the critical windows of PM 2.5 and PM 1 exposures. • PMs had greater effects on the second-born neonates than the first-born neonates. [ABSTRACT FROM AUTHOR]

• First to study the effect of temperature on morphological change of placenta. • A prospective birth cohort study was conducted in China. • Higher temperature was associated with decrease in placental weight and volume. • Higher temperature was related to increase in placental weight to birthweight ratio. • The effects of temperatures on placenta were stronger in the late pregnancy. The placenta performs crucial functions to ensure normal fetal development. Experimental studies have indicated associations between exposure to elevated temperatures during pregnancy and reduction in placental weight and volume. However, epidemiological studies in humans are lacking. To estimate the associations between prenatal exposure to ambient temperature with placental weight, volume, and the placental weight to birth weight ratio (PFR). We conducted a prospective birth cohort study using the Prenatal Environment and Offspring Health Cohort (PEOH Cohort) beginning in 2016 in Guangzhou, China. Women in early pregnancy were recruited and followed up during their hospitalization for childbirth. An inverse distance-weighted method was employed to estimate the average temperature exposure of every 4 weeks as well as the trimester-specific average temperature exposure at the individual's residential address. A generalized linear model was applied to estimate the effects of temperature exposure during pregnancy on the placental weight, volume, and PFR. A total of 4051 pregnant women were enrolled. Compared with the reference temperature of 20 °C, maternal exposure to 29 °C (95th centile) during late pregnancy was associated with an average of −6.03 g (95% confidence interval [CI]: −11.28 g, −0.78 g) in placental weight, −16.15 cm3 (95% CI: −26.24 cm3, −6.07 cm3) in placental volume, and 0.26 (95% CI: 0.07, 0.45) in PFR. The peak effects of high temperatures on placental weight, volume, and PFR were found from 29 to 32 weeks (β = –3.79 g, 95% CI: −8.39 g, 0.82 g), 37 to 40 weeks (β = −19.34 cm3, 95% CI: −30.99 cm3, −7.69 cm3), and 25 to 28 weeks (β = 0.35, 95% CI: 0.04, 0.66), respectively. Maternal exposure to elevated temperatures was associated with a decrease in placental weight and volume and an increase in PFR. The associations were stronger when exposures occurred during late pregnancy. [ABSTRACT FROM AUTHOR]

A small number of epidemiological studies have suggested the association of antimony (Sb) exposure with type 2 diabetes risk. However, little is known about the relationship between Sb exposure during pregnancy and risk of gestational diabetes mellitus (GDM). To investigate the associations of urinary Sb concentrations with GDM risk and blood glucose levels in pregnant women. We analyzed the baseline data of 1789 pregnant women enrolled in the Birth Cohort Study on Prenatal Environments and Offspring Health (PEOH) in Guangzhou, China. Sb concentrations in urine were measured by inductively coupled plasma mass spectrometry (ICP-MS). Logistic regression and analysis of covariance were used to evaluate associations of Sb exposure with GDM risk and blood glucose levels. A total of 437 (24.4%) women were diagnosed with GDM. The relative risk of GDM for women in the highest quartile of creatinine-corrected Sb (CC–Sb) concentrations was 1.55 [RR (95% CI) = 1.55 (1.12, 2.15), p -trend = 0.005], compared with women in the lowest quartile. Moreover, the women in the top quartile of CC-Sb levels had a 5.2% higher 1 h blood glucose and a 4.2% higher 2 h blood glucose than those in the bottom quartile. We also found an interactive effect between maternal age and CC-Sb on the risk of GDM (p -interaction < 0.001). This study suggested significant positive associations of Sb exposure with increased GDM risk and impaired blood glucose homeostasis in pregnant women, and the Sb-GDM association might be modified by maternal age. • Sb exposure during pregnancy was positively associated with increased GDM risk. • The Sb-GDM association might be modified by maternal age. • Sb exposure correlated with impaired blood glucose homeostasis during pregnancy. [ABSTRACT FROM AUTHOR]

Maternal exposures to ambient temperatures during pregnancy may increase the preterm birth (PTB) risk; however, which periods have stronger effects remain controversial. The effects of temperature exposure on PTB in first- and second-born neonate groups may be different during the new baby boom that has followed the Two-child Policy in China. We examined a birth cohort of 4928 pregnant women beginning in 2016 in Guangzhou, China. An inverse distance weighted method was used to estimate the temperature exposure at each individual residential address. A distribution lag non-linear model incorporating a Cox proportional hazard model was employed to estimate the effects of temperature exposure on PTB and test the effects modification of birth order related to the new baby boom. A total of 4101 pregnant women were included, of which 234 (5.7%) experienced PTB. Compared with the mean temperature (23.0 °C), we found a significantly higher risks of PTB associated with high temperatures (i.e, 30 °C [95th centile]) from the 4th to 8th, and 22nd to 27th gestational weeks. A peak effect was found during the 6th week (HR = 1.79, 95% CI: 1.26, 2.54) and 24th week (HR = 1.83, 95% CI: 1.27, 2.62). The risks of PTB were reduced for low temperatures (i.e. 14 °C [5th centile] versus 23.0 °C) from the 2nd to 10th and 20th to 26th gestational weeks, and the negative peak effect was found during the 4th week (HR = 0.43, 95% CI: 0.26, 0.72) and 23rd week (HR = 0.59, 95% CI: 0.43, 0.83). Stratification analyses showed that significant effects of 30 °C versus 23 °C on PTB were observed during the 4th to 8th weeks in the second-born neonate, and the peak effect was found in the 6th week (HR = 2.13, 95% CI: 1.31, 3.47). However, we did not find significant effects of 30 °C during the same weeks in the first-born neonate group. Maternal exposures to higher temperatures during pregnancy may increase the risk of PTB, and lower temperatures may decrease the risk of PTB. Stronger effects of temperature exposures during the first trimester on PTB risk were found among the second-born neonates than among the first-born neonates. [ABSTRACT FROM AUTHOR]

Although studies have assessed the associations of maternal exposure to ozone (O 3) during pregnancy with blood pressure and the risk of hypertensive disorders of pregnancy (HDP), the results were inconsistent. Furthermore, no studies have been conducted in China where the ambient O 3 concentration continuedly increased. The present study aimed to estimate the effects of maternal exposure to O 3 during pregnancy on the HDP risk, systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (PP). All participants of pregnant women were selected from the prospective birth cohort study on Prenatal Environments and Offspring Health conducted in Guangzhou, China. A spatiotemporal land-use-regression model was used to estimate individual monthly air pollution exposure from three months before pregnancy to childbirth date. Information on HDP, SBP, DBP and PP was obtained from maternal medical records. A Logistic regression model and a mixed linear model were used to estimate the associations of maternal exposure to O 3 with the risk of HDP and blood pressure (SBP, DBP and PP), respectively. We found significant associations of maternal exposure to O 3 during the third (OR = 1.31, 95%CI: 1.07, 1.60) and the second month (OR = 1.25, 95%CI: 1.02, 1.51) before pregnancy with the risk of HDP. Observed significantly positive associations of O 3 exposures with SBP, DBP and PP during the two months before pregnancy and during the early pregnancy. The peak effects of O 3 exposure on SBP, DBP and PP were respectively observed during the second month of pregnancy (β = 1.07 mmHg, 95%CI: 0.84, 1.31 mmHg), the first month before pregnancy (β = 0.40 mmHg, 95%CI: 0.21, 0.50 mmHg) and the second month of pregnancy (β = 0.78 mmHg, 95%CI: 0.59, 0.97 mmHg). Our results suggest that maternal exposure to O 3 were positively associated with blood pressure and the risk of HDP, and the period from three months before pregnancy to the first trimester might be the critical exposure window. • The first to study the associations of maternal exposures to O 3 with HDP in China. • A STLUR was used to assess individual O 3 exposure incorporating activity patterns. • Maternal exposure to O 3 were positively associated with HDP risk and blood pressure. • Three months before pregnancy and the first trimester might be the critical window. [ABSTRACT FROM AUTHOR]

Epidemiological studies have found that increased risk of preterm birth (PTB) is associated with higher prenatal exposure to PM 10 and PM 2.5 , but few studies have been conducted to assess the impacts of extremely fine particulate matter (PM 1) which may have more toxic effects than other types of ambient particulate air pollution (PM). Several studies have separately investigated the associations between DNA methylation and PTB risk and PM. Maternal LINE-1 methylation level negatively correlated with prenatal exposure to PM and risk of PTB. A comprehensive picture is lacking regarding the associations between prenatal exposure to PM, LINE-1 methylation, and risk of PTB. This study aimed to estimate the effects of exposure to ambient PM (PM 10 , PM 2.5 , and PM 1) of different sizes during pregnancy on risk of PTB, identify susceptible exposure windows, and illustrate the roles of LINE-1 methylation in the associations between PM and PTB risk. The Birth Cohort Study on Prenatal Environments and Offspring Health (PEOH) has been ongoing since 2016 in Guangzhou, China. A total of 4928 pregnant women were recruited during early pregnancy, and 4278 (86.8%) were successfully followed-up. Each individual weekly exposure to PM 10 and PM 2.5 from 3 months before pregnancy to childbirth was assessed using a spatiotemporal land use regression model, and the weekly PM 1 exposure was estimated by employing a generalized additive model. Maternal and cord blood LINE-1 methylation levels (%5mC) were tested using bisulfite-PCR pyrosequencing. A distributed lag nonlinear model incorporated with a Cox proportional hazard model was applied to assess the effect of weekly-specific maternal PM exposure on PTB risk, and a multiple-linear regression model was employed to investigate the associations between PM exposure and LINE-1 methylation levels of maternal and cord bloods. We also assessed the associations between LINE-1 methylation levels and PTB risk by using a logistic regression model. The risk of PTB was positively associated with PM 2.5 and PM 1 concentrations during the 12th to 20th gestational weeks, and the strongest association was in the fourth quartile (Q 4) versus the first quartile (Q 1) and observed during the 16th gestational week (PM 2.5 : harzard ratio [HR] = 1.18, 95%CI: 1.04–1.35, IQR = 11.94 μg/m3. PM 1 : HR = 1.20, 95%CI: 1.03–1.39, IQR = 11.36 μg/m3). We observed significantly negative associations of PM 10 (β = −0.51%5mC per 10 μg/m3, P = 0.014), PM 2.5 (β = −0.66%5mC per 10 μg/m3, P = 0.032) and PM 1 (β = −0.67%5mC per 10 μg/m3, P = 0.032) concentrations with cord blood LINE-1 methylation levels, and a negative association between PM 1 concentration and maternal LINE-1 methylation level (β = −0.86%5mC per 10 μg/m3, P = 0.034). Higher prenatal exposure to PM 1 and PM 2.5 during the 12th to 20th gestational weeks was associated with increased risk of PTB. Maternal and fetal LINE-1 methylation alternation might be an underlying mechanism of PM that increasing the risk of PTB. • Preterm birth (PTB) risk was positively associated with prenatal exposure to PM 2.5 and PM 1 , and a slightly greater effect was found for PM 1 compared with PM 2.5. • Gestational weeks 12 to 20 might be the critical window with the peak effect in the 16th week. • Fetal LINE-1 methylation was simultaneously associated with prenatal exposure to ambient particulate air pollution (PM) and PTB risk, indicating that LINE-1 methylation alternation might be an underlying mechanism of PM increasing PTB risk. However, the roles of maternal LINE-1 methylation may differ compared with their infants. [ABSTRACT FROM AUTHOR]