Intra-urban variability of long-term exposure to PM2.5 and NO2 in five cities in Colombia.

Rapidly urbanizing cities in Latin America experience high levels of air pollution which are known risk factors for population health. However, the estimates of long-term exposure to air pollution are scarce in the region. We developed intraurban land use regression (LUR) models to map long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in the five largest cities in Colombia. We conducted air pollution measurement campaigns using gravimetric PM2.5 and passive NO2 sensors for 2 weeks during both the dry and rainy seasons in 2021 in the cities of Barranquilla, Bucaramanga, Bogotá, Cali, and Medellín, and combined these data with geospatial and meteorological variables. Annual models were developed using multivariable spatial regression models. The city annual PM2.5 mean concentrations measured ranged between 12.32 and 15.99 µg/m3 while NO2 concentrations ranged between 24.92 and 49.15 µg/m3. The PM2.5 annual models explained 82% of the variance (R2) in Medellín, 77% in Bucaramanga, 73% in Barranquilla, 70% in Cali, and 44% in Bogotá. The NO2 models explained 65% of the variance in Bucaramanga, 57% in Medellín, 44% in Cali, 40% in Bogotá, and 30% in Barranquilla. Most of the predictor variables included in the models were a combination of specific land use characteristics and roadway variables. Cross-validation suggests that PM2.5 outperformed NO2 models. The developed models can be used as exposure estimate in epidemiological studies, as input in hybrid models to improve personal exposure assessment, and for policy evaluation.

Ethnoracial Disparities in Nitrogen Dioxide Pollution in the United States: Comparing Data Sets from Satellites, Models, and Monitors.

In the United States (U.S.), studies on nitrogen dioxide (NO2) trends and pollution-attributable health effects have historically used measurements from in situ monitors, which have limited geographical coverage and leave 66% of urban areas unmonitored. Novel tools, including remotely sensed NO2 measurements and estimates of NO2 estimates from land-use regression and photochemical models, can aid in assessing NO2 exposure gradients, leveraging their complete spatial coverage. Using these data sets, we find that Black, Hispanic, Asian, and multiracial populations experience NO2 levels 15-50% higher than the national average in 2019, whereas the non-Hispanic White population is consistently exposed to levels that are 5-15% lower than the national average. By contrast, the in situ monitoring network indicates more moderate ethnoracial NO2 disparities and different rankings of the least- to most-exposed ethnoracial population subgroup. Validating these spatially complete data sets against in situ observations reveals similar performance, indicating that all these data sets can be used to understand spatial variations in NO2. Integrating in situ monitoring, satellite data, statistical models, and photochemical models can provide a semiobservational record, complete geospatial coverage, and increasingly high spatial resolution, enhancing future efforts to characterize, map, and track exposure and inequality for highly spatially heterogeneous pollutants like NO2.

How does pollen production of allergenic species differ between urban and rural environments?

Pollen production is one plant characteristic that is considered to be altered by changes in environmental conditions. In this study, we investigated pollen production of the three anemophilous species Betula pendula, Plantago lanceolata, and Dactylis glomerata along an urbanization gradient in Ingolstadt, Germany. We compared pollen production with the potential influencing factors urbanization, air temperature, and the air pollutants nitrogen dioxide (NO2) and ozone (O3). While we measured air temperature in the field, we computed concentration levels of NO2 and O3 from a land use regression model. The results showed that average pollen production (in million pollen grains) was 1.2 ± 1.0 per catkin of Betula pendula, 5.0 ± 2.4 per inflorescence of Plantago lanceolata, and 0.7 ± 0.5 per spikelet of Dactylis glomerata. Pollen production was higher in rural compared to urban locations on average for B. pendula (+ 73%) and P. lanceolata (+ 31%), while the opposite was the case for D. glomerata (- 14%). We found that there was substantial heterogeneity across the three species with respect to the association of pollen production and environmental influences. Pollen production decreased for all species with increasing temperature and urbanization, while for increasing pollutant concentrations, decreases were observed for B. pendula, P. lanceolata, and increases for D. glomerata. Additionally, pollen production was found to be highly variable across species and within species-even at small spatial distances. Experiments should be conducted to further explore plant responses to altering environmental conditions.

Analysis of spatial distribution characteristics and main influencing factors of heavy metals in road dust of Tianjin based on land use regression models.

Land use regression (LUR) models are mainly used for the simulation and prediction of conventional atmospheric pollutants. Whether the LUR models can be expanded to study more toxic and hazardous pollutants (such as heavy metals) remains to be verified. Combined with the factors of road, land use type, population, pollution enterprise, meteorology, and terrain, the LUR models were used to simulate the spatial distribution characteristics of heavy metals in road dust and determine the main influencing factors. Samples of road surface dust were collected from 144 evenly distributed points in Tianjin, China, with 108 modelling points and 36 verification points. The R2 values of the LUR models of Cd, Cr, Cu, Ni, and Pb contents were 0.301, 0.412, 0.399, 0.496, and 0.377, and their error rates were 2.72%, 4.96%, 4.64%, 8.91%, and 4.94%, respectively. The error rates of the kriging interpolation models were 3.33%, 6.50%, 5.14%, 18.30%, and 22.87%, which were all greater than those of the LUR models. The estimation effect of the LUR models was more refined than that of the kriging interpolation models. The contents of most heavy metals (except Ni) in road dust of the central area in Tianjin were generally higher than those of the surrounding areas. The heavy metal contents in road dust of Tianjin were mainly affected by road variables and meteorological variables. The LUR models were suitable for small-scale spatial prediction of heavy metals in urban road dust within urban areas.

Ambient PM2.5 and clinically recognized early pregnancy loss: A case-control study with spatiotemporal exposure predictions.

BACKGROUND: Experimental research suggests that fine particulate matter (PM2.5) exposure might affect embryonic development. However, only few population-based studies have investigated the impact of maternal exposure to PM2.5 on the early pregnancy loss. OBJECTIVES: To estimate associations between clinically recognized early pregnancy loss (CREPL) and exposure to ambient PM2.5 at individual residences during peri-conception periods, with the aim to identify susceptible exposure time windows. METHODS: CREPL cases and normal early pregnancy controls (of similar age and gravidity presenting within one week, a total of 364 pairs) were recruited between July 2017 and July 2018 among women residing in Tianjin, China. Average ambient PM2.5 concentrations of ten exposure windows (4 weeks, 2 weeks and 1 week before conception; the first, second, third and fourth single week, the first and second 2-week periods, and the entire 4-week period after conception) at the women's residential addresses were estimated using temporally-adjusted land use regression models. Associations between PM2.5 exposures at specific peri-conception time windows and CREPL were examined using conditional logistic regression models, adjusted for covariates. RESULTS: Based on adjusted models, CREPL was significantly associated with a 10 µg/m3 increase in PM2.5 exposure during the second week after conception (OR = 1.15; 95% CI: 1.04, 1.27; p = 0.005), independent of effects at other time windows. There was also an association of CREPL with PM2.5 during the entire 4-week period after conception (OR = 1.22; 95% CI: 1.02, 1.46; p = 0.027). There was little evidence for associations with exposure during pre-conception exposure windows. CONCLUSIONS: Maternal exposures to ambient PM2.5 during a critical time window following conception are associated with CREPL, with the second week after conception possibly being the exposure window of most vulnerability. Future studies should focus on replicating these findings and on pathogenic mechanisms.

[Bibliometrics and visualization analysis of land use regression models in ambient air pollution research].

Objective: To quantitatively analyze the current status and development trends regarding the land use regression (LUR) models on ambient air pollution studies. Methods: Relevant literature from the PubMed database before June 30, 2017 was analyzed, using the Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.0). Keywords co-occurrence networks, cluster mapping and timeline mapping were generated, using the CiteSpace 5.1.R5 software. Relevant literature identified in three Chinese databases was also reviewed. Results: Four hundred sixty four relevant papers were retrieved from the PubMed database. The number of papers published showed an annual increase, in line with the growing trend of the index. Most papers were published in the journal of Environmental Health Perspectives. Results from the Co-word cluster analysis identified five clusters: cluster#0 consisted of birth cohort studies related to the health effects of prenatal exposure to air pollution; cluster#1 referred to land use regression modeling and exposure assessment; cluster#2 was related to the epidemiology on traffic exposure; cluster#3 dealt with the exposure to ultrafine particles and related health effects; cluster#4 described the exposure to black carbon and related health effects. Data from Timeline mapping indicated that cluster#0 and#1 were the main research areas while cluster#3 and#4 were the up-coming hot areas of research. Ninety four relevant papers were retrieved from the Chinese databases with most of them related to studies on modeling. Conclusion: In order to better assess the health-related risks of ambient air pollution, and to best inform preventative public health intervention policies, application of LUR models to environmental epidemiology studies in China should be encouraged.

Bibliometrics and visualization analysis of land use regression models in ambient air pollution research / 中华流行病学杂志

Objective To quantitatively analyze the current status and development trends regarding the land use regression (LUR) models on ambient air pollution studies.Methods Relevant literature from the PubMed database before June 30,2017 was analyzed,using the Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.0).Keywords co-occurrence networks,cluster mapping and timeline mapping were generated,using the CiteSpace 5.1.R5 software.Relevant literature identified in three Chinese databases was also reviewed.Results Four hundred sixty four relevant papers were retrieved from the PubMed database.The number of papers published showed an annual increase,in line with the growing trend of the index.Most papers were published in the journal of Environmental Health Perspectives.Results from the Co-word cluster analysis identified five clusters:cluster #0 consisted of birth cohort studies related to the health effects of prenatal exposure to air pollution;cluster #1 referred to land use regression modeling and exposure assessment;cluster #2 was related to the epidemiology on traffic exposure;cluster #3 dealt with the exposure to ultrafine particles and related health effects;cluster #4 described the exposure to black carbon and related health effects.Data from Timeline mapping indicated that cluster #0 and #1 were the main research areas while cluster #3 and #4 were the up-coming hot areas of research.Ninety four relevant papers were retrieved from the Chinese databases with most of them related to studies on modeling.Conclusion In order to better assess the health-related risks of ambient air pollution,and to best inform preventative public health intervention policies,application of LUR models to environmental epidemiology studies in Chinashould be encouraged.

Bibliometrics and visualization analysis of land use regression models in ambient air pollution research / 中华流行病学杂志

Objective To quantitatively analyze the current status and development trends regarding the land use regression (LUR) models on ambient air pollution studies.Methods Relevant literature from the PubMed database before June 30,2017 was analyzed,using the Bibliographic Items Co-occurrence Matrix Builder (BICOMB 2.0).Keywords co-occurrence networks,cluster mapping and timeline mapping were generated,using the CiteSpace 5.1.R5 software.Relevant literature identified in three Chinese databases was also reviewed.Results Four hundred sixty four relevant papers were retrieved from the PubMed database.The number of papers published showed an annual increase,in line with the growing trend of the index.Most papers were published in the journal of Environmental Health Perspectives.Results from the Co-word cluster analysis identified five clusters:cluster #0 consisted of birth cohort studies related to the health effects of prenatal exposure to air pollution;cluster #1 referred to land use regression modeling and exposure assessment;cluster #2 was related to the epidemiology on traffic exposure;cluster #3 dealt with the exposure to ultrafine particles and related health effects;cluster #4 described the exposure to black carbon and related health effects.Data from Timeline mapping indicated that cluster #0 and #1 were the main research areas while cluster #3 and #4 were the up-coming hot areas of research.Ninety four relevant papers were retrieved from the Chinese databases with most of them related to studies on modeling.Conclusion In order to better assess the health-related risks of ambient air pollution,and to best inform preventative public health intervention policies,application of LUR models to environmental epidemiology studies in Chinashould be encouraged.

[Applying temporally-adjusted land use regression models to estimate ambient air pollution exposure during pregnancy].

The impact of maternal air pollution exposure on offspring health has received much attention. Precise and feasible exposure estimation is particularly important for clarifying exposure-response relationships and reducing heterogeneity among studies. Temporally-adjusted land use regression (LUR) models are exposure assessment methods developed in recent years that have the advantage of having high spatial-temporal resolution. Studies on the health effects of outdoor air pollution exposure during pregnancy have been increasingly carried out using this model. In China, research applying LUR models was done mostly at the model construction stage, and findings from related epidemiological studies were rarely reported. In this paper, the sources of heterogeneity and research progress of meta-analysis research on the associations between air pollution and adverse pregnancy outcomes were analyzed. The methods of the characteristics of temporally-adjusted LUR models were introduced. The current epidemiological studies on adverse pregnancy outcomes that applied this model were systematically summarized. Recommendations for the development and application of LUR models in China are presented. This will encourage the implementation of more valid exposure predictions during pregnancy in large-scale epidemiological studies on the health effects of air pollution in China.

Applying temporally-adjusted land use regression models to estimate ambient air pollution exposure during pregnancy / 中华预防医学杂志

The impact of maternal air pollution exposure on offspring health has received much attention. Precise and feasible exposure estimation is particularly important for clarifying exposure-response relationships and reducing heterogeneity among studies. Temporally-adjusted land use regression (LUR) models are exposure assessment methods developed in recent years that have the advantage of having high spatial-temporal resolution. Studies on the health effects of outdoor air pollution exposure during pregnancy have been increasingly carried out using this model. In China, research applying LUR models was done mostly at the model construction stage, and findings from related epidemiological studies were rarely reported. In this paper, the sources of heterogeneity and research progress of meta-analysis research on the associations between air pollution and adverse pregnancy outcomes were analyzed. The methods of the characteristics of temporally-adjusted LUR models were introduced. The current epidemiological studies on adverse pregnancy outcomes that applied this model were systematically summarized. Recommendations for the development and application of LUR models in China are presented. This will encourage the implementation of more valid exposure predictions during pregnancy in large-scale epidemiological studies on the health effects of air pollution in China.