Land-use Model Research Articles

Costs of forest carbon sequestration in the presence of climate change impacts

Forests play a critical role in mitigating climate change, and, at the same time, are predicted to experience large-scale impacts of climate change that will affect the efficiency of forests in mitigation efforts. Projections of future carbon sequestration potential typically do not account for the changing economic costs of timber and agricultural production and land use change. We integrated a dynamic forward-looking economic optimization model of global land use with results from a dynamic global vegetation model and meta-analysis of climate impacts on crop yields to project future carbon sequestration in forests. We find that the direct impacts of climate change on forests, represented by changes in dieback and forest growth, and indirect effects due to lost crop productivity, together result in a net gain of 17 Gt C in aboveground forest carbon storage from 2000 to 2100. Increases in climate-driven forest growth rates will result in an 81%–99% reduction in costs of reaching a range of global forest carbon stock targets in 2100, while the increases in dieback rates are projected to raise the costs by 57%–132%. When combined, these two direct impacts are expected to reduce the global costs of climate change mitigation in forests by more than 70%. Inclusion of the third, indirect impact of climate change on forests through reduction in crop yields, and the resulting expansion of cropland, raises the costs by 11%–38% and widens the uncertainty range. While we cannot rule out the possibility of climate change increasing mitigation costs, the central outcomes of the simultaneous impacts of climate change on forests and agriculture are 64%–86% reductions in the mitigation costs. Overall, the results suggest that concerns about climate driven dieback in forests should not inhibit the ambitions of policy makers in expanding forest-based climate solutions.

Land-planning management based on multiple ecosystem services and simulation in tropical forests

Forest losses can lead to severe damage to ecosystem services (ESs), especially in the tropics. Tropical forests are widespread in southwestern China, and they experience continual effects of human activities (e.g., rubber boom). However, forest simulations of land planning have not yet been systematically conducted. Based on a future land-use simulation model, here, the spatio-temporal characteristics of four ES (i.e., soil retention, water yield, carbon fixation, and habitat quality) were examined, and three scenarios (i.e., natural development, rubber development, and ecological protection) were designed and evaluated during 2000 for Xishuangbanna (XSBN), southwestern China. The results showed that: (1) from 2000 to 2020, the average values of the ESs declined by 449.1 t for soil retention, 13.4 mm for water yield, 0.1 for habitat quality, and 0.1 kg C/m2 for carbon fixation; (2) the four ESs, with the exception of water yield, had synergistic relationships, and trade-off appeared on the margins of these synergistic relationships; (3) compared with the scenarios of natural development and rubber development, the environmental protection scenario was found to have high efficiency for protecting nature reserves and reducing fragmentation; and (4) the intensity of land-use change will accelerate the decrease of ESs, and it is essential for nature reserves and areas of northern XSBN to improve their level of environmental protection. This work not only further enriches the ES research from the ecological environment and land-planning points of view, but it also provides different planning perspectives for ES and forest scenarios. This is useful in methodical approaches to forest sustainability.

Particulate matter, albumin/creatinine ratio, and chronic kidney disease in a prospective cohort study

Background: Many studies have investigated the association between exposure to particulate matter (PM) and kidney dysfunction, but the relationship is inconsistent due to the variations in exposure assessment, the use of renal-function indices, and study design. Objectives: This study elucidated the associations between exposure to PM10, PM2.5, and PM1 with albumin/creatinine ratio (ACR) and the incidence of chronic kidney disease (CKD) in a community-based cohort in Taiwan. Methods: A total of 704 residents living in Taichung were recruited as study subjects at the baseline of 2004 and followed up to the end of 2018. Questionnaire and health examination was used to collect personal information and renal-function indices at baseline and follow-up. Land-use regression models were established to predict individual levels of PM10, PM2.5, and PM1 annually during studying periods. The multivariable linear regression and Cox regression were conducted to investigate the associations between PM exposures, ACR, and the incident CKD (creatinine-based estimated glomerular filtration rate, [eGFRcr] < 60ml/min/1.73m2). Results: We found that an increase of 1 µg/m3 in PM10, PM2.5, and PM1 was associated with the decreased ACR of 1.10±1.14 mg/g (p=0.080), 4.66±2.17 mg/g (p=0.032), and 6.05±2.82 mg/g (p=0.032), respectively. The similar associations were identified for the cumulative exposure of 1 µg/m3-year to PM10, PM2.5, and PM1, which decreased the ACR of 0.14±0.10 mg/g (p=0.157), 0.55±0.27 mg/g (p=0.040), and 0.72±0.35 mg/g (p=0.040), respectively. Per 1 µg/m3 increase in PM10, PM2.5, and PM1 was associated with the elevated risk of 1.07-fold (95% confidence interval [CI]=1.00-1.15, p=0.055), 1.19-fold (95% CI=1.04-1.37, p=0.011), and 1.26-fold (95% CI=1.05-1.50, p=0.011) in the incident CKD, respectively. Conclusions: The present study showed that exposure to PM2.5 and PM1 was associated with the decreased renal function to increase the risk of developing CKD.

Exposure to ambient ultrafine particles and allergic sensitization in children up to 16 years

Background and aim: Few epidemiological studies so far have investigated the role of long-term exposure to ultrafine particles (UFP) in allergy development. The purpose of this study was to assess the association between UFP exposure and allergic sensitization to inhalant and food allergens in children up to 16 years old in the Netherlands. Methods: 2295 participants of the prospective PIAMA birth cohort with specific IgE measurements to common inhalant and food allergens at ages 4, 8 or 12 or to common inhalant allergens at age 16 were included in the study. Annual average UFP concentrations were estimated for the home addresses at birth and at the time of the IgE measurements using land-use regression models. Generalized estimating equations were used for the assessment of overall and age-specific associations between UFP exposure and allergic sensitization. Additionally, single- and two-pollutant models with NO2, PM2.5, PM2.5 absorbance and PM10 were assessed. Results: We found no significant associations between UFP exposure and allergic sensitization to inhalant and food allergens (OR (95% CI) ranging from 1.02 (0.95 – 1.10) to 1.05 (0.98 – 1.12), per IQR increment). In contrast, NO2, PM2.5, PM2.5 absorbance and PM10 showed significant associations with sensitization to food allergens (OR (95% CI) ranging from 1.09 (1.00–1.20) to 1.23 (1.06–1.43) per IQR increment) but not with sensitization to inhalant allergens (OR (95% CI) ranging from 0.98 (0.90-1.07) to 1.08 (0.93-1.25) per IQR increment). For NO2, PM2.5 and PM2.5 absorbance, the associations with sensitization to food allergens persisted in two-pollutant models with UFP. Conclusion: This study found no association between annual average exposure to UFP and allergic sensitization in children up to 16 years of age. NO2, PM2.5, PM2.5 absorbance and PM10 were associated with sensitization to food allergens. Key words: ultrafine particles, IgE sensitization, inhalant and food allergens, children, air pollution, allergy

Prenatal Exposure to Air Pollution and Childhood Lipid Levels: The Rhea Birth Cohort (Greece)

BACKGROUND AND AIM: Abnormal blood lipid profiles in childhood are an important precursor to atherosclerosis and cardiovascular disease. Air pollution has been associated with lipid levels in animal models; however, evidence from human studies remains insufficient and inconclusive. We evaluated the associations of pregnancy exposure to air pollution with serum lipid profiles during childhood in the Rhea birth cohort, Greece. METHODS: Residential exposure to particulate matter <10 μm (PM10) and <2.5 μm (PM2.5) during pregnancy was estimated with temporally adjusted land-use regression models. We measured triglycerides (TG), total cholesterol (TC), HDL- and LDL- cholesterol at 4, 6, and 11 years. Abnormal lipid levels in children were defined according to the US NHLBI guidelines. We assessed associations with multivariate regression models and effect modification by sex. RESULTS: Prenatal PM2.5 and PM10 levels ranged between 13.0 and 18.7 μg/m3 and 34.9 and 54.6 μg/m3, respectively. PM10 concentrations over the 4th quartile were associated with lower TG levels overall (β= -5.25 mg/dL, 95% CI: -10.38, -0.11) and lower TC and TG levels in girls (β= -6.87 mg/dL, 95% CI: -13.78, 0.05; β= -9.03 mg/dL, 95% CI: -17.05, -1.01 respectively) at 4 years. At 11 years, higher prenatal PM10 levels were associated with both higher levels of TC and LDL (β= 7.75 mg/dL, 95% CI: -0.17, 15.68; β= 7.40 mg/dL, 95% CI: 0.36, 14.45) but also higher risk for elevated TC and LDL. Associations at 11 years were stronger in girls, indicating a sex-dependent effect. Associations with PM2.5 were in the same direction but not significant. No associations were observed at 6 years. CONCLUSIONS: These results shed new light on the importance of controlling air pollution, which can contribute to abnormal blood lipid levels during childhood, an independent risk factor for adult cardiovascular disease. KEYWORDS: prenatal, air pollution, lipids, children

Residential greenness, air pollution, and incident ischemic heart disease: A prospective cohort study in China

Background and aim: Green living environments are associated with beneficial health outcomes, whereas higher air pollution exposure might increase the risk of chronic diseases. Moreover, few studies have explored the interaction between residential greenness and air pollution on the risk of ischemic heart disease (IHD). Therefore we aimed to estimate the associations of residential greenness and air pollution with IHD and their interaction. Methods: We performed a prospective cohort study that included 29,141 adult participants recruited from Yinzhou District, Ningbo, China. Normalized Difference Vegetation Index (NDVI) around each participant’s residence was calculated to measure residential greenness exposure. Land-use regression models were conducted to estimate long-term individual exposure to air pollutants, including nitrogen dioxide (NO2) and particulate matter with aerodynamic diameters ≤ 2.5 μm (PM2.5) and ≤10 μm (PM10). Cox proportional hazard models were used to calculate the hazard ratios (HRs) and 95% confidence intervals (95% CIs) for the association of residential greenness and air pollutants with the risk of incident IHD. Results: During 101,172.5 person-years of follow-up, 1,392 incident IHD cases were reported in the study population. Residential greenness, expressed as an interquartile range (IQR) increase in NDVI within 300m, was inversely associated with incident IHD. However, long-term air pollution exposures were associated with higher IHD incidence. Mediation analyses suggested that the beneficial effect of residential greenness on incident IHD could be partly mediated by reducing the exposure to PM2.5. Conclusion: Higher greenness was associated with decreased risk of IHD, while air pollutants were positively associated with incident IHD. Meanwhile, residential greenness may decrease the risk of IHD by reducing PM2.5 exposure. Keywords: Residential greenness; Air pollution; Ischemic heart disease; Cohort study; Effect mediation

In-utero exposure to air pollution and blood pressure in children from the Rhea birth cohort study, Greece

BACKGROUND AND AIM: High blood pressure (BP) in childhood has been linked with hypertension and impaired cardiovascular health within adulthood. Exposure to air pollution has been associated with elevated BP in children but only a few studies evaluated the persistence of these associations into early adolescence. We investigated the association between pregnancy exposure to air pollution and BP through childhood in the Rhea birth cohort in Greece. METHODS: Residential exposure to particulate matter <10 μm (PM10) and <2.5 μm (PM2.5) during pregnancy was estimated with temporally adjusted land-use regression models. We measured systolic (SBP) and diastolic (DBP) blood pressure at 4, 6, and 11 years. Hypertension was defined as DBP or SBP ≥95th sex-, age- and height-percentiles. Generalized estimating equation and multivariate regression models assessed the associations (per 5 µg/m³) with repeated and single time-point measures, respectively. We also tested effect modification by sex and maternal smoking. RESULTS: Median (IQR) levels of PM2.5 and PM10 during pregnancy were 14.4 (2.4) μg/m3 and 35.6 (2.3) μg/m3, respectively. PM10 was associated with higher SBP and DBP in boys but lower in girls (p-interaction= 0.025 and 0.036) at 4 years. We also observed higher risk for hypertension overall (1.62; 95% CI: 1.04, 2.53) and in boys (2.01; 95% CI: 1.07, 3.79), and in children born to non-smoking mothers during pregnancy (2.14; 95% CI: 1.25, 3.66). Higher PM2.5 was associated with lower SBP in boys at 6 (β= -3.75 mmHg; 95% CI: -7.14, -0.36) and 11 years (β= -4.49 mmHg; 95% CI: -9.09, 0.11; p-interaction= 0.068 and 0.072). Sex differences in BP trajectories were also observed. CONCLUSIONS: This study suggests that prenatal air pollution exposure may affect blood pressure during childhood. Further research is needed to understand sex differences and the role of co-exposure to maternal smoking. KEYWORDS: Blood pressure, childhood, air pollution, prenatal

Associations of air pollution with COVID-19 test positivity, hospitalisations, and mortality: observational evidence from UK Biobank

Background and aim: Individual-level studies with adjustment for COVID-19 risk factors suggest a weak positive association of long-term air pollution exposure (particulate matter and nitrogen dioxide) with COVID-19 positive test, hospitalisation, and mortality. The evidence, however, remains limited and mechanisms remain unclear. We aimed to investigate these associations within UK Biobank, and to examine the role of underlying chronic disease as a potential mechanism. Methods: UK Biobank COVID-19 positive test results were ascertained via Public Health England and general practitioner record linkage, COVID-19 hospitalisations via Hospital Episode Statistics, and COVID-19 mortality via Office for National Statistics mortality records from March – December 2020. We used annual average outdoor air pollution from a 2010 land-use regression model at residential addresses within England of UK Biobank participants (n = 424,721) and obtained important COVID-19 risk factors from baseline UK Biobank questionnaire responses (2006-2010) and general practitioner record linkage. We used logistic regression models to assess associations of air pollution with COVID-19 outcomes after adjustment for relevant confounders and conducted multiple sensitivity analyses. Results: We found positive associations of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) with COVID-19 positive test result after adjustment for confounders, with odds ratio of 1.06 (95% confidence intervals (CI) = 1.03, 1.09), and 1.06 (95% CI = 1.02, 1.09) per interquartile range increase, respectively. PM2.5 and NO2 were positively associated with COVID-19 hospitalisations and deaths in minimally adjusted models but not in fully adjusted models. No associations for PM10 were found. In analyses with additional adjustment for pre-existing chronic disease, effect estimates were not substantially attenuated, indicating that underlying chronic disease may not fully explain associations. Conclusion: We found some evidence that long-term exposure to PM2.5 and NO2 was associated with a COVID-19 positive test, though not with COVID-19 hospitalisations or deaths. Keywords: PM2.5, NO2, coronavirus

Metal Constituents of Fine Particulate Matter Mass and Incident Depression: A Community-based Cohort Study in China

BACKGROUND AND AIM: Little is known about the effects of chemical constituents of fine particulate matter (particles with aerodynamic diameter ≤ 2.5 μm, PM2.5). This study aims to estimate the associations between constituents of PM2.5 and incident depression. METHODS: This large cohort study recruited the local adults in Ningbo, a southeastern coastal city in China. Depression cases were ascertained from local Health Information System. Daily PM2.5 samples were collected at seven monitoring sites for the 10th-16th seven consecutive days and further measured for ten PM2.5-bound metals. Land-use regression models were conducted to predict the residential PM2.5-bound metals exposure. We conducted three cox proportional hazards models which respectively employed single constituent, constituent adjusting for PM2.5 and constituent residual calculated by PM2.5 as exposures to estimate the effect of constituents on incident depression. Environmental Risk Score (ERS) and Weighted Quintile Score (WQS) were utilized to estimate the overall effect of all metals. RESULTS: In fully adjusted constituent models, the higher concentrations of ambient Lead(Pb), Nickel(Ni), Mercury(Hg), Chromium(Cd) and Beryllium(Be) were significantly associated with higher risk of incident depression. In ERS analyses, one main effect (Tl), three squared terms (Hg, Cd and Al) and twelve pairwise interactions (Tl & Pb, Tl & Be, et al) were selected by adaptive elastic-net (AENET) for construction of ERS of incident depression-related ambient metal mixtures. The ERS index was positively and significantly associated with depression incidence. And similar results were found for WQS index which suggesting that Al, Sb, Hg, Tl, Mn and Be contributed the most to the association. CONCLUISIONS: These findings highlighted the associations of exposure to PM2.5-bound metals with incident depression. Further studies are needed to confirm the findings and examine the underlying mechanisms. KEYWORDS: PM2.5-bound Metal, depression, Cox Proportional Hazardous Regression, ERS, WQS.

Association of sound pressure levels and frequency components of road traffic noise with prevalent depression in Taichung, Taiwan

The World Health Organization has reported that the prevalence of mental illness is 4% in the global population. Epidemiological studies have reported a relationship between road traffic noise exposure and depression, but the association between noise frequency components and depression remains unclear. This cross-sectional study investigated the associations between road traffic noise exposure and its frequency components with prevalent depression. A total of 3,200 residents living in Taichung who participated in the Taiwan Biobank between 2010 and 2017, were included as study participants. We used land-use regression models to evaluate individual annual average values of A-weighted equivalent sound level over 24 h (Leq,24h) and particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) using the geographic information system. Multiple logistic regression was applied to estimate the odds ratios (ORs) for depression after adjusting for potential risk factors and PM2.5. An interquartile range increase in Leq,24h at full frequency (4.7 dBA), 1000 Hz (5.2 dB), and 2000 Hz (4.8 dB) was significantly associated with an elevated risk for depression with ORs of 1.62 (95% confidence interval [CI]: 1.03, 2.55), 1.58 (95% CI: 1.05, 2.37), and 1.58 (95% CI:1.03, 2.43), respectively, by controlling for PM2.5. The high-exposure group (≥ the median of noise levels) at full frequency, 500 Hz, and 2000 Hz had an increased risk for depression with ORs of 1.87 (95% CI: 1.03, 3.39), 1.94 (95% CI: 1.03, 3.65), and 1.86 (95% CI: 1.03, 3.37), respectively, compared with the low-exposure group (< the median of noise levels) after adjustment for PM2.5. Road traffic noise exposure may be associated with an increased prevalence of depression, particularly at 500 to 2000 Hz.

Long-term exposure to elemental components of fine particulate matter and natural and cause-specific mortality in a Danish nationwide administrative cohort

BACKGROUND AND AIM: Fine particulate matter (PM₂.₅) is an established risk factor for premature death, but it remains unclear which components and sources are most responsible. We investigated the associations between long-term exposure to PM₂.₅ components and mortality from all-natural causes, cardiovascular disease (CVD), respiratory diseases (RD), lung cancer, diabetes, chronic kidney disease (CKD), dementia, and psychiatric disorders in a Danish nationwide administrative cohort. METHODS: We followed all Danish residents aged ≥30 at January 1, 2000 (3,081,244) until December 31, 2017. Residential annual mean exposure PM₂.₅ components levels (copper, iron, zinc, sulfur, nickel, vanadium, silicon, and potassium) were estimated by Europe-wide land-use regression models at a 100×100m scale, developed within the "Effects of Low-Level Air Pollution: A Study in Europe” project, with two modeling approaches: supervised linear regression (SLR) and random forest (RF). We used Cox proportional hazard models to evaluate the associations between each component and mortality, adjusting for demographic, socioeconomic factors, and PM₂.₅ mass. RESULTS: The cohort accumulated 46,992,890 person-years, and 803,373 died from natural causes. For natural mortality, we observed significant positive associations [hazard ratios (HRs); 95% confidence intervals per interquartile range increase] with SLR-silicon (1.04; 1.03-1.05) and SLR-potassium (1.03; 1.02-1.04) and with RF- iron (1.02; 1.01-1.02), RF-zinc (1.02; 1.01-1.03), RF-nickel (1.01; 1.00-1.02), RF-vanadium (1.02; 1.01-103), RF-silicon (1.01; 1.00-1.02), and RF-potassium (1.06; 1.05-1.07). Potassium and silicon were most strongly associated with all causes of death except CKD and diabetes, respectively, with the highest HRs observed for psychiatric disorders. Furthermore, iron was associated with RD, lung cancer, CKD, and psychiatric disorders, zinc with RD, CKD, and lung cancer, and nickel and vanadium with lung cancer. CONCLUSIONS: We present novel findings that different PM₂.₅ components were relevant to different causes of death. Potassium and silicon seemed most consistently associated with mortality in Denmark. KEYWORDS: Long-term exposure, PM₂.₅ components, mortality

Association between ambient air pollution exposure and plasma β-amyloid levels in the French Three-City study : preliminary results

Background: Existing research suggests an association between air pollution exposure and Alzheimer’s Disease (AD). However, underlying biological mechanisms are still unknown. We aimed to evaluate if air pollution exposure was associated with plasma β-amyloid (Aβ) levels. Methods: We selected 287 participants of the Three-City study who were cognitively unimpaired at 12-year follow-up. Aβ levels were measured on plasma samples stored at baseline and at 10 years (INNO-BIA plasma Aβ forms kits (Innogenetics)). Land-use regression models were used to estimate fine particulate matter (PM2.5), nitrogen dioxide (NO2) and black carbon (BC) exposure at participants’ residential address over the 5-year preceding the baseline visit. Linear regression was performed to assess the association of each pollutant with plasma Aβ levels at baseline and with their evolution over time. Results: Participants’ median age at baseline was 68.8 (IQR: 5.1), and 56.1% were women. Median levels (IQR) of plasma Aβ at baseline were 158.8 (37.5) pg/mL for Aβ1-40 and 41.4 (11.9) pg/mL for Aβ1-42. Median Aβ1-42/Aβ1-40 ratio was 0.255 (IQR: 0.065). We found that exposure to the three air pollutants was associated with decreased Aβ1-42/Aβ1-40 ratio at baseline. For instance, an IQR increase in PM2.5 (1.8 µg/m3) was associated with a 0.0119 decrease in the Aβ ratio (95% CI: -0.0209; -0.0029). There were no significant associations between air pollutants and Aβ evolution over time. Conclusion: In our cognitively unimpaired population, exposure to air pollution was associated with decreased plasma Aβ ratio at baseline. These results seem to corroborate previous ones on air pollution detrimental effect on cognition, as a decreased plasma Aβ ratio may predict AD. Further longitudinal studies using cutting edge methods to quantify plasmatic Aβ levels and brain Aβ deposits are still needed to better understand the influence of air pollution on the neuropathological process in AD. Keywords: Air pollution, Alzheimer’s Disease, Biomarkers

Longitudinal associations between metabolites and long-term exposure to ambient air pollution: results from the KORA cohort study

BACKGROUND AND AIM: Long-term exposure to air pollution is associated with a vast number of adverse health outcomes, while the underlying mechanisms remain unclear. Therefore, this study aimed to investigate changes in serum metabolites associated with long-term exposure to air pollution in a longitudinal study design. METHODS: We used data from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999-2001) and two follow-up examinations (F4: 2006-08 and FF4: 2013-14). Mass-spectrometry-based targeted metabolomics was used to quantify metabolites among serum samples. Only participants with repeated metabolites measurements were included in current analysis. Land-use regression models were used to estimate annual average concentrations of ultrafine particles, particles with a diameter < 10 µm, coarse particles (PMcoarse), fine particles, PM2.5 absorbance (a proxy of elemental carbon related to traffic exhaust, PM2.5abs), nitrogen oxides (NO2, NOx), and ozone at individuals’ residences. We applied covariate-adjusted linear mixed-effects regression models to examine associations between long-term exposure to air pollution and metabolites. RESULTS: Among 9,620 observations from 4,261 KORA participants, we included 5,772 (60.0%) observations from 2,583 (60.6%) participants in this analysis. Out of 108 metabolites that passed stringent quality control among these study waves, we identified nine significant negative associations between phosphatidylcholines (PCs) and PM2.5abs, PMcoarse, and NO2, at a Benjamini-Hochberg false discovery rate (FDR) corrected p-value < 0.05. The strongest association was seen for an increase of 0.27 μg/m3 (interquartile range) in PM2.5abs and decreased phosphatidylcholine acyl-alkyl C36:3 (PC ae C36:3) concentrations [percent change in the geometric mean: -2.5% (95% confidence interval: -3.6%, -1.5%)]. CONCLUSIONS: Our study suggested that long-term exposure to air pollution is associated with metabolic alterations, particularly in PCs with unsaturated long-chain fatty acids. These findings might provide new insights into potential mechanisms for air pollution-related adverse health outcomes. KEYWORDS: long-term air pollution, targeted metabolomics, phosphatidylcholine

Association between long-term exposure to air pollution and cardio-metabolic phenotypes – A MRI-data based analysis

Background and Aim: Long-term exposure to ambient air pollution is supposed to affect both the cardiovascular system and adipose tissue (AT) metabolism. However, its role in the early subclinical stages of development before manifestation of cardiometabolic disease is unclear. Thus, we aimed to investigate the impact of long-term exposure to air pollution on cardiometabolic phenotypes derived from magnetic resonance imaging (MRI). Methods: Phenotypes of left (LV) and right cardiac ventricle, whole body AT, and organ AT were obtained by whole-body MRI in 400 participants of the population-based KORA cohort. Land-use regression models were used to estimate residential long-term exposures to several air pollutants, including particulate matter (PM) with different diameters and nitrogen oxides (e.g. NO₂). Associations between exposures and MRI phenotypes were modeled using confounder-adjusted linear regression. Additionally, we performed several stratified analyses, e.g. for diabetes status, hypertension, or age group. Results: Participants’ mean age was 56±9 years, 42% were female. Increasing levels of several air pollutants were associated with decreased LV wall thickness; a 6.0μg/m³ IQR (interquartile range) increase in NO₂ was significantly associated with a -1.89% decrease (95% confidence interval [95%-CI]: -3.70;-0.09) in mean global LV wall thickness. We also found significant associations between various air pollutants and increased cardiac AT. A 2.1μg/m³ IQR increase in PM₁₀ was associated with an 8.83% [95%-CI: 1.19;16.49] increase in mean total epi- and pericardial AT. Similar effects were seen for mean diastolic (8.53% [95%-CI: 0.84;16.23]) and systolic (8.57% [95% CI: 0.10;16.17]) pericardial AT, respectively. We saw a similar trend for liver and renal AT. Associations with whole-body AT were only detected in participants with diabetes in the stratified analysis. Conclusions: Our results indicate that long-term exposure to air pollution is associated with subclinical cardiometabolic disease states, particularly in metabolically vulnerable subgroups. Keywords: cardiometabolic phenotypes, ambient air pollution, magnetic resonance imaging.

Spatio-temporal modeling of airborne pollen concentrations

Keywords: Pollen, Machine Learning, Allergic Disease Abstract Background and aim High concentrations of airborne pollen trigger seasonal allergies and possibly severe adverse respiratory and cardiovascular health events. Predicting pollen concentration accurately is valuable for epidemiological studies to determine the effects on cardiovascular, respiratory, and cognitive health. We aimed to develop a spatiotemporal land-use regression model for pollen, predicting daily concentrations at a fine spatial resolution of 1x1km across Switzerland between 2003 and 2020. Methods Daily pollen concentrations for hazel, alder, ash, birch, and grasses were available from 14 sites. We considered a range of spatial (elevation, tree type), temporal (date, season, month, week and day of the year, national daily pollen concentration) and spatiotemporal predictors (wind speed, wind direction, temperature, precipitation, relative humidity, satellite-observed Normalized Difference Vegetation Index (NDVI), and land-use (CLC, Landsat satellite) to explain variation in total pollen concentration for five specific pollen species. We applied a range of feature engineering techniques to encode categorical variables (land-use) and fill in missing values (Landsat). We applied a random forest model with 5-fold cross-validation. Results The median grass pollen concentration was 24 pollen/m3 (P5-P95 range 0-187 pollen/m3) during the main grass pollen season (May-July for all years). Preliminary results of a model predicting grass pollen concentration achieved an overall R2 of 0.74 and a root mean squared error (RMSE) of 24.12 pollen/m3 (cross-validation). Temperature, humidity, wind speed, NDVI, Landsat, average national daily pollen concentration and date features were the most important predictors for grass pollen concentration. Conclusions Building upon national observed pollen concentrations and using random forest machine learning, these spatiotemporal pollen models will serve to estimate individual residential pollen exposure. Resulting estimates will enable us to study respiratory and cardiovascular mortality and hospital admissions using historical data from the Swiss National Cohort and the Swiss Federal Office of Statistics.

Air quality and children allergic diseases and neurodevelopmental disorders - A nationwide birth cohort follow-up approach

Background and Aim Air pollution was associated with decreased lung function and intelligence. However, sizeable prospective follow-up studies with pre-and postnatal exposure considerations are scarce. The research is urgent due to the potential life-long lung and brain functional effects and the new WHO air quality guideline. In addition, extensive data-based investigations of two distinguished disorders might help examine potential confounders. The goal is to investigate prenatal exposure to nitrogen dioxide (NO2) and postnatal allergic diseases and neurodevelopmental disorders in a national-wide birth cohort. Methods We used the National Health Insurance Research Database (NHIRD) and the Taiwan Maternal and Child Health Database (TMCHD) to establish a national birth cohort born from 2007-to 2011 to follow the cumulative incidence of children's allergic diseases neurodevelopmental disorders till 2017. As a result, there were 759,142 infant-mother pairs finally included in the cohort without significant abnormalities. In addition, we utilized hybrid land-use regression models and machine learning methods to assess daily exposure to nitrogen dioxide (NO2) air pollutants, adopting the data of 76 national air-monitoring stations. Results The children experienced significantly increased allergic diseases [Hazard Ratio (HR), atopic dermatitis: 1.42 (Confidence Interval 1.38~1.45); asthma 1.21 (CI 1.19~1.23); allergic rhinitis: 1.22 (1.20~1.24)] and neurodevelopmental disorders [HR, autism: 1.52 (1.41~1.63); attention deficit hyperactivity disorder: 1.41 (1.36~1.46)] but not for epilepsy [HR, 1.04 (0.97~1.12)] with the 3rd tertile exposure to NO2 concentration (≥ 20.0 ppb) as compared to the lower (≤ 15.1 ppb) prenatally, adjusted for paid insurance amount, maternal smoking status, and parental history of the individual examined diseases in the Cox's model. Conclusion The incidence of children's allergic diseases and neurodevelopmental disorders increased significantly at the 3rd tertile of NO2 concentration before birth. Therefore, our result also corresponded to the new WHO guideline of optimized 20 ppb for NO2.

Effects of land-use change on the volume of water flow into the Mun Bon reservoir in Nakhon Ratchasima Province, Thailand

The land-use patterns in watershed areas in the Chorakhe Hin Sub-district, Khon Buri District, Nakhon Ratchasima Province, Thailand, have been found to change from forest areas. Different agricultural areas cause variations in the amount of water that flows from the stream into the reservoir, potentially leading to future water shortages. This study was conducted to explore the effects of land-use change on the volume of water flow into the Mun Bon Reservoir, Chorakhe Hin Sub-district, Khon Buri District, Nakhon Ratchasima Province, Thailand. The model analysis techniques, namely Markov’s Chain CLUES and SWAT, were employed to predict the effects of land-use patterns in the area of the upper Mun River on the volume of water flow into the Mun Bon reservoir. According to the predictions obtained based on the land-use models, forest areas may be converted into cassava plantations by 2029. When the comparative effects were considered, the normal volume of water flowing into the Mun Bon reservoir was found to be equivalent to 96 million cubic meter per year. The predicted volume before Christ (A.D.) 2029 is 30 million cubic meter. Accordingly, the water volume in the Mun Bon reservoir would be lower than that derived from the usual land-use patterns.

Ambient air pollution and risk of incident dementia in older men living in a region with relatively low concentrations of pollutants: The Health in Men Study

BackgroundIn areas with moderate to severe air pollution, pollutant concentrations are associated with dementia risk. It is unclear whether the same relationship is present in regions with lower ambient air pollution. ObjectiveTo determine whether exposure to air pollution is associated with risk of incident dementia in general, and Alzheimer's disease and vascular dementia in particular, in older men living in a relatively low ambient air pollution region. MethodsThe cohort comprised 11,243 men residing in Perth, Australia. Participants were aged ≥65 years and free of a dementia diagnosis at time of recruitment in 1996–1999. Incident dementia was identified from recruitment to 2018 via ICD diagnosis codes and subsequent study waves. Concentrations for three air pollutants, nitrogen dioxide (NO2), fine particulate matter less than 2.5 μm in diameter (PM2.5), and black carbon (BC) were estimated at participants’ home addresses using land-use regression models. We used Cox proportional hazards regression models adjusting for smoking status, physical activity, BMI, education, and socio-economic status. ResultsOf 3053 (27.2%) incident cases of dementia, 1670 (54.7%) and 355 (11.6%) had documented Alzheimer's disease and vascular dementia. The average concentration of NO2 was 13.5 (SD 4.4) μg/m3, of PM2.5 was 4.54 (SD 1.6) μg/m3 and of BC was 0.97 (SD 0.29) ×10−5 m−1. None of the air pollutants were associated with incident dementia or Alzheimer's disease. In the unadjusted model, increased exposure to PM2.5 was associated with an increased risk of vascular dementia (for a 5 μg/m3 increase: HR 1.62, 95% CI 1.13, 2.31). However, this association was attenuated following adjustment for confounders (HR 1.39, 95% CI 0.93, 2.08). NO2 and BC were not associated with vascular dementia incidence. DiscussionExposure to air pollution is not associated with increased risk of incident dementia in older men living in a region with relatively low ambient air pollution.

Primate conservation in the Arc of Deforestation: a case study of Vieira's titi monkey Plecturocebus vieirai

AbstractFifty years of deforestation in the Arc of Deforestation have put at risk species survival, ecosystem services and the stability of biogeochemical cycles in Amazonia, with global repercussions. In response, we need to understand the diversity, distribution and abundance of flagship species groups, such as primates, which can serve as umbrella species for broad biodiversity conservation strategies and help mitigate climate change. Here we identify the range, suitable habitat areas and population size of Vieira's titi monkey Plecturocebus vieirai and use it as an emblematic example to discuss biodiversity conservation and climate change mitigation in one of the largest deforestation frontiers. Our findings show that deforestation for agriculture and cattle-ranching expansion is the major threat to P. vieirai and is responsible for present (56%) and projected (14%) reductions in habitat area and population size. We also found that human-driven climate change affects the P. vieirai niche negatively, triggering habitat degradation and further population decline even inside protected areas. Primate watching can be a profitable alternative to forest exploitation on private, public or Indigenous lands in the Arc of Deforestation and is a way to shift the traditional, predatory extraction of natural resources from Amazonia towards sustainable land use based on biodiversity conservation at local, regional and global scales, local people's welfare and climate change mitigation. New models of land use and income generation are required to protect the unique natural and human heritages of the Arc of Deforestation and the life-supporting ecosystem services and products provided by Amazonia.

The temporal and spatial pattern evolution of land-use carbon emissions in China coastal regions and its response to green economic development

Carbon emissions based on land use change have attracted extensive attention from scholars, but the current land use carbon emission accounting model is still relatively rough. Despite the continuous promotion of China’s ecological civilization strategy, whether green economic development promotes carbon emission reduction remains to be studied. This study uses the Exploratory Spatial-temporal Data Analysis (ESTDA) framework system to revise the land-use carbon emission accounting model; it integrates the NDVI adjustment index and systematically analyzes the spatial and temporal patterns and evolutionary path characteristics of carbon emissions from 2000 to 2020 for 130 prefecture-level cities in the eastern coastal region of China, a high carbon emission region. The spatial econometric model is further used to explore the impact of green economy development on carbon emissions. The results show that the spatial distribution of carbon sources and sinks in the eastern coastal cities demonstrates a year-on-year increase during the study period. The spatial distribution of carbon sources is higher in the north than in the south, and the economically developed regions are more elevated than less developed economic areas. Net carbon emissions show prominent spatial clustering characteristics. The south has a more stable internal spatial structure than the north, and the inland has a more stable internal spatial structure than the coast. Green economic development can significantly reduce carbon emission intensity and has a significant spatial spillover effect. The findings imply that policy-makers need to consider the spatial and temporal distribution and spatial correlation of carbon emissions among cities; they can achieve carbon emission reduction by formulating a more reasonable green economy development approach and implementing regional linkages.