The spatial and temporal disaggregation models of high-accuracy vehicle emission inventory

A high-accuracy gridding vehicle emission inventory is not only the foundation for developing refined emission control strategies but a necessary input to air quality model as well. An accurate approach to the spatiotemporal disaggregation is the key step to improving the accuracy of gridding emission inventories. The existing spatial disaggregation method considers relatively fewer impact factors, lacking adequate correlation analysis among impact factors. Additionally, the existing temporal disaggregation method does not correspond with the actual travel behavior of residents. This paper proposes a multi-factor spatial disaggregation model by principal component analysis (PCAM), based on a correlation analysis of the main impact factors. Further, a new temporal disaggregation model is proposed based on the congestion delay index combined with the traffic flow fundamental model (CDITF). The results from a case study in Jinan show that the square of correlation coefficients (RSQ) between the model- disaggregated NO2 emissions based on PCAM and the monitored NO2 concentration increased by 34.4% compared to the traditional disaggregation model based on the standard road length, and the RSQ for CO increased by 13%; the NMD and NME of the simulation results based on CMAQ model compared to standard road length model decrease by approximately 33.7% and 35.5%, respectively. The trend of the monthly, daily, and hourly variations of NO2 and CO emissions disaggregated by the proposed temporal disaggregation model is quite consistent with that of the monitored concentration data. The PCAM method and the CDITF proposed in this paper are more in line with the actual situation using the cumulative emissions on road sections. The vehicle emissions in Jinan are found to be concentrated in the center of each district and county and near high-grade roads. The disaggregation results in areas with large road slopes are more realistic for considering road slope factors. The trend of the monthly, daily, and hourly variations of NO2 and CO emissions disaggregated by the proposed temporal disaggregation model is quite consistent with that of the monitored concentration data, however, the monitored concentration data presents a certain degree of time lag. The proposed spatiotemporal disaggregation model in this paper improves the accuracy of gridding vehicle emission inventory, which is of a great significance for developing precise control strategies of vehicle emissions and improving the urban air quality in general.

Spatial-Temporal Analysis of Factors Influencing the Median Urine Iodine Concentration of 8-10-year-old Children in Xinjiang, China 25 Years after Implementation of the Salt Iodization Policy

The iodine status of children has improved and stabilized since China implemented salt iodization measures 25 years ago, but routine monitoring of iodine cannot reflect regional factors that influence the iodine level in children. Therefore, we conducted a regional spatial-temporal analysis of children's median urinary iodine concentration (MUIC) and searched for possible factors that might affect children's iodine levels by mining monitoring data. We analyzed data from Xinjiang collected as part of the "Iodine Deficiency Disease National Monitoring Program" from 2017 to 2020. The study population consisted of 76,268 children who participated in the study. We used global autocorrelation analysis to determine whether the MUIC of children was spatially clustered, local autocorrelation analysis to identify specific clustering areas, local cold and hot spot analysis to verify the reliability of the local autocorrelation results, and a spatial lag model to identify factors affecting the children's MUIC. The MUIC values were 217.70, 227.00, 230.67, and 230.67 µg/L in 2017, 2018, 2019, and 2020, respectively. Global autocorrelation analysis showed that the MUIC of all children in the study was significantly related to region (Z scores all > 1.96, P values all < 0.05) from 2017 to 2020. Partial auto-correlation analysis showed that counties with clusters of high values were mostly concentrated in the southwestern region of Xinjiang, whereas counties with clusters of low values were located in the northern part of Xinjiang. Partial cold spot and hot spot analysis showed the same trend, and there are more overlapping districts and counties in 4 years. Three-dimensional trend analysis indicated that children from the western part of Xinjiang had high levels of urinary iodine. According to spatial lag model, urine iodine concentration level is positively correlated with thyroid volume, average salary, and urbanization rate classification. The MUIC of 8-10-year-old children in Xinjiang was spatially clustered and related to geographic region. Our results show that spatial analysis of survey data combined with geographic technology and public health data can accurately identify areas with abnormal iodine concentrations in children. Additionally, understanding the factors that influence iodine levels in the human population is conducive to improving monitoring methods.

Measurement and spatial-temporal analysis of coupling coordination development between green finance and environmental governance in China

To direct financial resources for achieving the goal of sustainable development, Chinese government has devoted increasing efforts to developing green finance. However, few studies explored the relationship between green finance and environmental governance. Thus, this paper first theoretically discusses the interactive connection between green finance and environmental governance. And then we construct two comprehensive indicator systems and use entropy method to calculate green finance index (GFI) and environmental governance index (EGI) for 30 provinces of China from 2004 to 2020. The theoretical analysis unveils the complementary and mutual reinforcing relationship of the interaction between green finance and environmental governance through green industry. Using the data of GFI and EGI, the coupling coordination degree of green finance and environmental governance (CCDGE) is measured by coupling coordination model. The trend analysis discovers that GFI is increasing over time while EGI starts decreasing from 2013. Although GFI has grown more rapidly than EGI, but the development of green finance still lags behind environmental governance because of its short history. Just because of the uncoordinated development between green finance and environmental governance, CCDGE has been hovering in the moderate coupling coordination stage for a long time and still has a great distance to the high coupling coordination level. These findings imply that the relationship between green finance and environmental governance is still in a state of disorderly development that restricts each other. Furthermore, the findings of spatial-temporal analysis show there are obvious regional differences in GFI and EGI and the interactive effect between green finance and environmental governance. Specifically, GFI and EGI in eastern China are the highest, while CCDGE presents with a ladder decline status of "eastern region > central region > northeast region > west region." Our findings provide vital references for policymakers to promote the coupling coordination development between green finance and environmental governance.

Towards the analysis of urban livability in China: spatial-temporal changes, regional types, and influencing factors

The increasing drift of urbanization and its impact on urban human settlements are of major concern for China cities. Therefore, demystifying the spatial-temporal patterns, regional types, and affecting factors of urban livability in China is beneficial to urban planning and policy making regarding the construction of livable cities. In accordance with its connotation and denotation, this study develops a systematic evaluation and analysis framework for urban livability. Drawing on the panel data of 40 major cities in China from 2005 to 2019, an empirical research was further conducted. The results show that urban livability in China has exhibited a rising trend during the period, but this differs across dimensions. The levels of urban security and environmental health are lower than those of the three other dimensions. Spatially, cities with higher livability are mainly distributed in the first quadrant divided by the Hu Line and Bole-Taipei Line. Cities in the third quadrant are equipped with the lowest livability. In addition, the 40 major cities can be divided into five categories, and obvious differences exist in terms of the geographical distribution, overall livability level, and sub-dimensional characteristics of the different types. Furthermore, the results of the System GMM estimator indicate that the overall economic development exerts an inhibiting effect on the improvement of urban livability in present-day China, but this logical effect exhibits obvious heterogeneity in different time periods and diverse city scales. Finally, there are also differences in the influencing direction and degree of specific economic determinants.

Tuberculosis burden caused by migrant population in Eastern China: evidence from notification records in Zhejiang Province during 2013-2017

Background

Internal migrants have an enormous impact on tuberculosis (TB) epidemic in China. Zhejiang Province, as one of the developed areas, also had a heavy burden caused by TB.

Methods

In this study, we collected all cases in Zhejiang Province through the TB Management Information System from 2013 to 2017. Description analysis and Spatio-temporal analysis using R software and ArcGIS were performed to identify the epidemiological characteristics and clusterings, respectively.

Results

48,756 individuals in total were notified with TB among the migrant population (TBMP), accounting for one-third of all cases identified. The primary sources of TB from migrants outside the province were from Guizhou, Sichuan, and Anhui. Wenzhou, Taizhou, and Lishui were the three mainly outflowing cities among the intra-provincial TBMP and Hangzhou as the primarily inflowing city. Also, results implied that the inconsistency of the TBMP in spatial analysis and the border area of Quzhou and Lishui city had the highest risk of TB occurrence among the migrants. Additionally, one most likely cluster and four secondary clusters were identified by the spatial–temporal analysis.

Conclusion

The effective control of TB in extra-provincial MP was critical to lowering the TB burden of MP in Zhejiang Province. Also, it is suggested that active TB screening for migrant employees outflowed from high epidemic regions should be strengthened, and further traceability analysis needs to be investigated to clarify the mechanism of TB transmission in clustered areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07071-5.

Spatial-temporal clusters of pediatric perforated appendicitis in California

BACKGROUND

Perforated appendicitis is a well-documented child health disparity. Geographic patterns in perforated appendicitis exist in several United States regions, but such patterns have not been described in California. We aimed to analyze spatial-temporal patterns of pediatric perforated appendicitis and identify population characteristics contributing to these cluster patterns.

METHODS

We geocoded risk-adjusted perforated appendicitis rates per 1000 appendicitis cases in patients 1-17 years from 2005-2015 in California. We performed a space-time cube analysis to identify hot spot trends. We performed logistic regression to estimate rural classification associated with spatial-temporal hot spots and multivariate analysis to assess effects of socioeconomic factors.

RESULTS

In 2005-2015, 43,888 cases of pediatric perforated appendicitis occurred in California. Median risk-adjusted perforated appendicitis rate was 312 per 1000 appendicitis cases. We identified 11 spatial-temporal hot spots of perforated appendicitis. Rural micropolitan counties had 14 times higher odds of being classified as a hot spot (p<0.05, 95% CI 1-185). Poverty was a significant predictor of high perforated appendicitis median risk-adjusted rate (p<0.004).

CONCLUSIONS

We identified 11 California hot spots of perforated appendicitis that persisted across a ten-year time span. Incorporating geography alongside our understanding of socioeconomic factors is a critical step in addressing this important child health disparity.

Spatial-temporal analysis of net primary production (NPP) and its relationship with climatic factors in Iran

Fluctuations of the climate variables have increased in the recent years. These fluctuations are different in each climatic region. Net primary production (NPP) indicating the plant growth and carbon stabilization over period of time is influenced by these fluctuations. Investigation of the variations in the NPP and analysis of its relationship with the climatic and environmental variables can play a key role in determining the effects of fluctuations of climatic variables on the NPP. Therefore, the present study was conducted to investigate the spatiotemporal changes in the NPP and its correlation with precipitation rate and temperature during 2000-2014 based on the annual NPP estimates determined by the moderate resolution imaging spectroradiometer (MODIS) sensor and precipitation and temperature data of the synoptic stations in eight climate regions in Iran. The slope of variations in the NPP was calculated in these climatic regions, and then, the changes in the NPP trend at two confidence levels of 95 and 99% were investigated based on the pixel-based method using the Mann-Kendall test. The sensitivity of NPP to climatic variables of temperature and precipitation was also estimated by calculating the correlation. The results showed the significant spatial distribution of NPP in the whole region under study indicating a declining trend from north to south and from west to east directions. The results also indicated the nonlinear variations in the temporal distribution of NPP. The annual mean NPP was found to follow the climatic boundaries in the climatic regions except for climate region 2, and region with the higher annual mean precipitation had higher annual mean NPP. Analysis of the trend by the Mann-Kendall method revealed that 3.2% of the pixels in the whole region followed a certain trend. Among the pixels, 70% of them followed a negative trend and the remaining 30% followed a positive trend. The greatest number of pixels with a certain trend was found in the Gulf of Oman coast climate region so that 93% of the pixels had a positive trend. The lowest number of pixels with a certain trend was observed in eastern Alborz foothills so that 87% of the pixels showed a negative trend. Slope variations of the NPP in the whole region varied from - 35 to 46 gC m² year^-1. The eastern plateau had the highest negative slope variations among the climate regions, and the highest positive slope variation of 42% was observed in the highlands climate region. In general, the precipitation rate and temperature showed a mean partial coefficient of 0.22 and 0.02, respectively, and the correlation between the NPP and temperature and precipitation was different in each climatic region. The temperature was negatively correlated with the NPP in four climatic regions with higher annual mean temperatures and in other climatic regions; it had a weak positive correlation. Therefore, the sensitivity of NPP to precipitation and temperature was different in each climatic region.

Cholera hotspots and surveillance constraints contributing to recurrent epidemics in Tanzania

OBJECTIVE

We described the dynamics of cholera in Tanzania between 2007 and 2017 and assessed the weaknesses of the current surveillance system in providing necessary data in achieving the global roadmap to 2030 for cholera control.

RESULTS

The Poisson-based spatial scan identified cholera hotspots in mainland Tanzania. A zero-inflated Poisson regression investigated the relationship between the incidence of cholera and available demographic, socio-economic and climatic exposure variables. Four cholera hotspots were detected covering 17 regions, home to 28 million people, including the central regions and those surrounding the Lakes Victoria, Tanganyika and Nyaza. The risk of experiencing cholera in these regions was up to 2.9 times higher than elsewhere in the country. Regression analyses revealed that every 100 km of water perimeter in a region increased the cholera incidence by 1.5%. Due to the compilation of surveillance data at regional level rather than at district, we were unable to reliably identify any other significant risk factors and specific hotspots. Cholera high-risk populations in Tanzania include those living near lakes and central regions. Successful surveillance require disaggregated data available weekly and at district levels in order to serve as data for action to support the roadmap for cholera control.

Spatial-temporal trend for mother-to-child transmission of HIV up to infancy and during pre-Option B+ in western Kenya, 2007-13

Introduction

Using spatial–temporal analyses to understand coverage and trends in elimination of mother-to-child transmission of HIV (e-MTCT) efforts may be helpful in ensuring timely services are delivered to the right place. We present spatial–temporal analysis of seven years of HIV early infant diagnosis (EID) data collected from 12 districts in western Kenya from January 2007 to November 2013, during pre-Option B+ use.

Methods

We included in the analysis infants up to one year old. We performed trend analysis using extended Cochran–Mantel–Haenszel stratified test and logistic regression models to examine trends and associations of infant HIV status at first diagnosis with: early diagnosis (<8 weeks after birth), age at specimen collection, infant ever having breastfed, use of single dose nevirapine, and maternal antiretroviral therapy status. We examined these covariates and fitted spatial and spatial–temporal semiparametric Poisson regression models to explain HIV-infection rates using R-integrated nested Laplace approximation package. We calculated new infections per 100,000 live births and used Quantum GIS to map fitted MTCT estimates for each district in Nyanza region.

Results

Median age was two months, interquartile range 1.5–5.8 months. Unadjusted pooled positive rate was 11.8% in the seven-years period and declined from 19.7% in 2007 to 7.0% in 2013, p < 0.01. Uptake of testing ≤8 weeks after birth was under 50% in 2007 and increased to 64.1% by 2013, p < 0.01. By 2013, the overall standardized MTCT rate was 447 infections per 100,000 live births. Based on Bayesian deviance information criterion comparisons, the spatial–temporal model with maternal and infant covariates was best in explaining geographical variation in MTCT.

Discussion

Improved EID uptake and reduced MTCT rates are indicators of progress towards e-MTCT. Cojoined analysis of time and covariates in a spatial context provides a robust approach for explaining differences in programmatic impact over time.

Conclusion

During this pre-Option B+ period, the prevention of mother to child transmission program in this region has not achieved e-MTCT target of ≤50 infections per 100,000 live births. Geographical disparities in program achievements may signify gaps in spatial distribution of e-MTCT efforts and could indicate areas needing further resources and interventions.

A spatial-temporal analysis of hotels in urban tourism destination

This study investigates the spatial associations of urban tourism phenomena by using GIS and statistical methods to examine the relationships between hotels and land use types, attractions, transportation facilities, and the economic variables of the tertiary planning units in which the hotels are located. Hong Kong is used as an example. The study first introduces the spatial characteristics of hotels and attractions development in Hong Kong. A geographical information system is then used to map hotels and investigate the characteristics of the land use, attractions, and transport facilities around hotels. The spatial relationships are then analyzed with a set of logistic regression models. The results reveal that commercial land type and the number of attractions around hotels are significantly related to the distribution of upper-grade hotels in Hong Kong. The determinants vary over time and the spatial structure changes accordingly. The analysis is important theoretically as it enriches the methodologies for analyzing the relationships between hotels and urban structure, and for conceptualizing and identifying tourism functional zones. It is important for practitioners as it provides useful information for selecting sites for hotels.