Long-term exposure to fine particulate matter constituents and cognitive impairment among older adults: An 18-year Chinese nationwide cohort study.

BACKGROUND: Although growing evidence has shown independent links of long-term exposure to fine particulate matter (PM2.5) with cognitive impairment, the effects of its constituents remain unclear. This study aims to explore the associations of long-term exposure to ambient PM2.5 constituents' mixture with cognitive impairment in Chinese older adults, and to further identify the main contributor. METHODS: 15,274 adults ≥ 65 years old were recruited by the Chinese Longitudinal Healthy Longevity Study (CLHLS) and followed up through 7 waves during 2000-2018. Concentrations of ambient PM2.5 and its constituents (i.e., black carbon [BC], organic matter [OM], ammonium [NH4+], sulfate [SO42-], and nitrate [NO3-]) were estimated by satellite retrievals and machine learning models. Quantile-based g-computation model was employed to assess the joint effects of a mixture of 5 PM2.5 constituents and their relative contributions to cognitive impairment. Analyses stratified by age group, sex, residence (urban vs. rural), and region (north vs. south) were performed to identify vulnerable populations. RESULTS: During the average 3.03 follow-up visits (89,296.9 person-years), 4294 (28.1%) participants had developed cognitive impairment. The adjusted hazard ratio [HR] (95% confidence interval [CI]) for cognitive impairment for every quartile increase in mixture exposure to 5 PM2.5 constituents was 1.08 (1.05-1.11). BC held the largest index weight (0.69) in the positive direction in the qg-computation model, followed by OM (0.31). Subgroup analyses suggested stronger associations in younger old adults and rural residents. CONCLUSION: Long-term exposure to ambient PM2.5, particularly its constituents BC and OM, is associated with an elevated risk of cognitive impairment onset among Chinese older adults.

Systemic autoimmune rheumatic diseases and multiple industrial air pollutant emissions: A large general population Canadian cohort analysis.

BACKGROUND: Past investigations of air pollution and systemic autoimmune rheumatic diseases (SARDs) typically focused on individual (not mixed) and overall environmental emissions. We assessed mixtures of industrial emissions of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) and SARDs onset in Ontario, Canada. METHODS: We assembled an open cohort of over 12 million adults (without SARD diagnoses at cohort entry) based on provincial health data for 2007-2020 and followed them until SARD onset, death, emigration, or end of study (December 2020). SARDs were identified using physician billing and hospitalization diagnostic codes for systemic lupus, scleroderma, myositis, undifferentiated connective tissue disease, and Sjogren's. Rheumatoid arthritis and vasculitis were not included. Average PM2.5, NO2, and SO2 industrial emissions from 2002 to one year before SARDs onset or end of study were assigned using residential postal codes. A quantile g-computation model for time to SARD onset was developed for the industrial emission mixture, adjusting for sex, age, income, rurality index, chronic obstructive pulmonary disease (as a proxy for smoking), background (environmental overall) PM2.5, and calendar year. We conducted stratified analyses across age, sex, and rurality. RESULTS: We identified 43,931 new SARD diagnoses across 143,799,564 person-years. The adjusted hazard ratio for SARD onset for an increase in all emissions by one decile was 1.018 (95% confidence interval 1.013-1.022). Similar positive associations between SARDs and the mixed emissions were observed in most stratified analyses. Industrial PM2.5 contributed most to SARD risk. CONCLUSIONS: Industrial air pollution emissions were associated with SARDs risk.

Long-term exposure to fine particulate matter and ozone and the onset of systemic autoimmune rheumatic diseases: an open cohort study in Quebec, Canada.

OBJECTIVES: To estimate associations between fine particulate matter (PM2.5) and ozone and the onset of systemic autoimmune rheumatic diseases (SARDs). METHODS: An open cohort of over 6 million adults was constructed from provincial physician billing and hospitalization records between 2000 and 2013. We defined incident SARD cases (SLE, Sjogren's syndrome, scleroderma, polymyositis, dermatomyositis, polyarteritis nodosa and related conditions, polymyalgia rheumatic, other necrotizing vasculopathies, and undifferentiated connective tissue disease) based on at least two relevant billing diagnostic codes (within 2 years, with at least 1 billing from a rheumatologist), or at least one relevant hospitalization diagnostic code. Estimated PM2.5 and ozone concentrations (derived from remote sensing and/or chemical transport models) were assigned to subjects based on residential postal codes, updated throughout follow-up. Cox proportional hazards models with annual exposure levels were used to calculate hazard ratios (HRs) for SARDs incidence, adjusting for sex, age, urban-versus-rural residence, and socioeconomic status. RESULTS: The adjusted HR for SARDS related to one interquartile range increase in PM2.5 (3.97 µg/m3) was 1.12 (95% confidence interval 1.08-1.15), but there was no clear association with ozone. Indirectly controlling for smoking did not alter the findings. CONCLUSIONS: We found associations between SARDs incidence and PM2.5, but no relationships with ozone. Additional studies are needed to better understand interplays between the many constituents of air pollution and rheumatic diseases.

Sunlight Exposure, Sun-Protective Behavior, and Anti-Citrullinated Protein Antibody Positivity: A General Population-Based Study in Quebec, Canada.

OBJECTIVE: To examine associations between sunlight exposure and anti-citrullinated protein antibodies (ACPAs) using general population data in Quebec, Canada. METHODS: A random sample of 7,600 individuals (including 786 subjects who were ACPA positive and 201 self-reported rheumatoid arthritis [RA] cases) from the CARTaGENE cohort was studied cross-sectionally. All subjects were nested in 4 census metropolitan areas, and mixed-effects logistic regression models were used to calculate odds ratios (ORs) and 95% confidence intervals (95% CIs) for ACPA positivity related to sunlight exposure, adjusting for sun-block use, industrial fine particulate matter (PM2.5 ) exposures, smoking, age, sex, French Canadian ancestry, and family income. We also performed sensitivity analyses excluding subjects with RA, defining ACPA positivity by higher titers, and stratifying by age and sex. RESULTS: The adjusted ORs and 95% CIs did not suggest conclusive associations between ACPA and sunlight exposure or sun-block use, but robust positive relationships were observed between industrial PM2.5 emissions and ACPA (OR 1.19 per µg/m3 [95% CI 1.03-1.36] in primary analyses). CONCLUSION: We did not see clear links between ACPA and sunlight exposure or sun-block use, but we did note positive associations with industrial PM2.5 . Future studies of sunlight and RA (or ACPA) should take air pollution exposures into account.

Fine particulate matter components and interstitial lung disease in rheumatoid arthritis.

BACKGROUND: Exposure to ambient fine particulate matter with an aerodynamic diameter <2.5 µg·m-3 (PM2.5) is a risk factor for pulmonary and systemic autoimmune diseases; however, evidence on which PM2.5 chemical components are more harmful is still scant. Our goal is to investigate potential associations between major PM2.5 components and interstitial lung disease (ILD) onset in rheumatoid arthritis (RA). METHODS: New-onset RA subjects identified from a US healthcare insurance database (MarketScan) were followed for new onset of RA-associated ILD (RA-ILD) from 2011 to 2018. Annual concentrations of ambient PM2.5 chemical components (i.e. sulfate, nitrate, ammonium, organic matter, black carbon, mineral dust and sea salt) were estimated by combining satellite retrievals with chemical transport modelling and refined by geographically weighted regression. Exposures from 2006 up to 1 year before ILD onset or end of study were assigned to subjects based on their core-based statistical area or metropolitan division codes. A novel time-to-event quantile-based g (generalised)-computation approach was used to estimate potential associations between RA-ILD onset and the exposure mixture of all seven PM2.5 chemical components adjusting for age, sex and prior chronic obstructive pulmonary disease (as a proxy for smoking). RESULTS: We followed 280 516 new-onset RA patients and detected 2194 RA-ILD cases across 1 394 385 person-years. The adjusted hazard ratio for RA-ILD onset was 1.54 (95% CI 1.47-1.63) per every decile increase in all seven exposures. Ammonium, mineral dust and black carbon contributed more to ILD risk than the other PM2.5 components. CONCLUSION: Exposure to components of PM2.5, particularly ammonium, increases ILD risk in RA.

Tree characteristics and environmental noise in complex urban settings - A case study from Montreal, Canada.

Field studies have shown that dense tree canopies and regular tree arrangements reduce noise from a point source. In urban areas, noise sources are multiple and tree arrangements are rarely dense. There is a lack of data on the association between the urban tree canopy characteristics and noise in complex urban settings. Our aim was to investigate the spatial variation of urban tree canopy characteristics, indices of vegetation abundance, and environmental noise levels. Using Light Detection and Ranging point cloud data for 2015, we extracted the characteristics of 1,272,069 public and private trees across the island of Montreal, Canada. We distinguished needle-leaf from broadleaf trees, and calculated the percentage of broadleaf trees, the total area of the crown footprint, the mean crown centroid height, and the mean volume of crowns of trees that were located within 100m, 250m, 500m, and 1000m buffers around 87 in situ noise measurement sites. A random forest model incorporating tree characteristics, the normalized difference vegetation index (NDVI) values, and the distances to major urban noise sources (highways, railways and roads) was employed to estimate variation in noise among measurement locations. We found decreasing trends in noise levels with increases in total area of the crown footprint and mean crown centroid height. The percentages of increased mean squared error of the regression models indicated that in 500m buffers the total area of the crown footprint (29.2%) and the mean crown centroid height (12.6%) had a stronger influence than NDVI (3.2%) in modeling noise levels; similar patterns of influence were observed using other buffers. Our findings suggest that municipal initiatives designed to reduce urban noise should account for tree features, and not just the number of trees or the overall amount of vegetation.

Long-term ozone exposure and mortality from neurological diseases in Canada.

BACKGROUND: There is increasing interest in the health effects of air pollution. However, the relationships between ozone exposure and mortality attributable to neurological diseases remain unclear. OBJECTIVES: To assess associations of long-term exposure to ozone with death from Parkinson's disease, dementia, stroke, and multiple sclerosis. METHODS: Our analyses were based on the 2001 Canadian Census Health and Environment Cohort. Census participants were linked with vital statistics records through 2016, resulting in a cohort of 3.5 million adults/51,045,700 person-years, with 8,500/51,300/43,300/1,300 deaths from Parkinson's/dementia/stroke/multiple sclerosis, respectively. Ten-year average ozone concentrations estimated by chemical transport models and adjusted by ground measurements were assigned to subjects based on postal codes. Cox proportional hazards models were used to calculate hazard ratios (HRs) for deaths from the four neurological diseases, adjusting for eight common demographic and socioeconomic factors, seven environmental indexes, and six contextual covariates. RESULTS: The fully adjusted HRs for Parkinson's, dementia, stroke, and multiple sclerosis mortalities related to one interquartile range increase in ozone (10.1 ppb), were 1.09 (95% confidence interval 1.04-1.14), 1.08 (1.06-1.10), 1.06 (1.04-1.09), and 1.35 (1.20-1.51), respectively. The covariates did not influence significance of the ozone-mortality associations, except airshed (i.e., broad region of Canada). During the period of 2001-2016, 5.66%/5.01%/ 3.77%/19.11% of deaths from Parkinson's/dementia/stroke/multiple sclerosis, respectively, were attributable to ozone exposure. CONCLUSIONS: We found positive associations between ozone exposure and mortality due to Parkinson's, dementia, stroke, and multiple sclerosis.

Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia.

The robust estimate and forecast capability of random forests (RF) has been widely recognized, however this ensemble machine learning method has not been widely used in mosquito-borne disease forecasting. In this study, two sets of RF models were developed at the national (pooled department-level data) and department level in Colombia to predict weekly dengue cases for 12-weeks ahead. A pooled national model based on artificial neural networks (ANN) was also developed and used as a comparator to the RF models. The various predictors included historic dengue cases, satellite-derived estimates for vegetation, precipitation, and air temperature, as well as population counts, income inequality, and education. Our RF model trained on the pooled national data was more accurate for department-specific weekly dengue cases estimation compared to a local model trained only on the department's data. Additionally, the forecast errors of the national RF model were smaller to those of the national pooled ANN model and were increased with the forecast horizon increasing from one-week-ahead (mean absolute error, MAE: 9.32) to 12-weeks ahead (MAE: 24.56). There was considerable variation in the relative importance of predictors dependent on forecast horizon. The environmental and meteorological predictors were relatively important for short-term dengue forecast horizons while socio-demographic predictors were relevant for longer-term forecast horizons. This study demonstrates the potential of RF in dengue forecasting with a feasible approach of using a national pooled model to forecast at finer spatial scales. Furthermore, including sociodemographic predictors is likely to be helpful in capturing longer-term dengue trends.

Long-term exposure to a mixture of industrial SO2, NO2, and PM2.5 and anti-citrullinated protein antibody positivity.

BACKGROUND: Studies of associations between industrial air emissions and rheumatic diseases, or diseases-related serological biomarkers, are few. Moreover, previous evaluations typically studied individual (not mixed) emissions. We investigated associations between individual and combined exposures to industrial sulfur dioxide (SO2), nitrogen dioxide (NO2), and fine particles matter (PM2.5) on anti-citrullinated protein antibodies (ACPA), a characteristic biomarker for rheumatoid arthritis (RA). METHODS: Serum ACPA was determined for 7600 randomly selected CARTaGENE general population subjects in Quebec, Canada. Industrial SO2, NO2, and PM2.5 concentrations, estimated by the California Puff (CALPUFF) atmospheric dispersion model, were assigned based on residential postal codes at the time of sera collection. Single-exposure logistic regressions were performed for ACPA positivity defined by 20 U/ml, 40 U/ml, and 60 U/ml thresholds, adjusting for age, sex, French Canadian origin, smoking, and family income. Associations between regional overall PM2.5 exposure and ACPA positivity were also investigated. The associations between the combined three industrial exposures and the ACPA positivity were assessed by weighted quantile sum (WQS) regressions. RESULTS: Significant associations between individual industrial exposures and ACPA positivity defined by the 20 U/ml threshold were seen with single-exposure logistic regression models, for industrial emissions of PM2.5 (odds ratio, OR = 1.19, 95% confidence intervals, CI: 1.04-1.36) and SO2 (OR = 1.03, 95% CI: 1.00-1.06), without clear associations for NO2 (OR = 1.01, 95% CI: 0.86-1.17). Similar findings were seen for the 40 U/ml threshold, although at 60 U/ml, the results were very imprecise. The WQS model demonstrated a positive relationship between combined industrial exposures and ACPA positivity (OR = 1.36, 95% CI: 1.10-1.69 at 20 U/ml) and suggested that industrial PM2.5 may have a closer association with ACPA positivity than the other exposures. Again, similar findings were seen with the 40 U/ml threshold, though 60 U/ml results were imprecise. No clear association between ACPA and regional overall PM2.5 exposure was seen. CONCLUSIONS: We noted positive associations between ACPA and industrial emissions of PM2.5 and SO2. Industrial PM2.5 exposure may play a particularly important role in this regard.

Estimation of anthropogenic heat emissions in China using Cubist with points-of-interest and multisource remote sensing data.

Rapid urbanization and industrialization in China stimulated the great increase of energy consumption, which leads to drastic rise in the emission of anthropogenic waste heat. Anthropogenic heat emission (AHE) is a crucial component of urban energy budget and has direct implications for investigating urban climate and environment. However, reliable and accurate representation of AHE across China is still lacking. This study presented a new machine learning-based top-down approach to generate a gridded anthropogenic heat flux (AHF) benchmark dataset at 1 km spatial resolution for China in 2010. Cubist models were constructed by fusing points-of-interest (POI) data of varying categories and multisource remote sensing data to explore the nonlinear relationships between various geographic predictors and AHE from different heat sources. The strategy of developing specific models for different components and exploiting the complementary features of POIs and remote sensing data generated a more reasonable distribution of AHF. Results showed that the AHF values in urban centers of metropolises over China range from 60 to 190 W m-2. The highest AHF values were observed in some heavy industrial zones with value up to 415 W m-2. Compared with previous studies, the spatial distribution of AHF from different heating components was effectively distinguished, which highlights the potential of POI data in improving the precision of AHF mapping. The gridded AHF dataset can serve as input of urban numerical models and can help decision makers in targeting extreme heat sources and polluters in cities and making differentiated and tailored strategies for emission mitigation.

Tracking the origin of early COVID-19 cases in Canada.

The original coronavirus disease 2019 (COVID-19) outbreak in Wuhan, China has become a global pandemic. By tracking the earliest 118 COVID-19 cases in Canada, we produced a Voronoi treemap to show the travel origins of the country's earliest COVID-19 cases. By March 11, 2020, even though the majority (64.1%) of the world's COVID-19 confirmed cases still had their origin in China, only 7.6% of Canada's first 118 COVID-19 cases were related to travelers from China. The most commonly reported travel history among the 118 cases related to the Middle East, the United States, and Europe. Thus, in retrospect, broadening of early screening tools and travel restrictions to countries and regions outside China may have helped control global COVID-19 spread.

Investigating associations between anti-nuclear antibody positivity and combined long-term exposures to NO2, O3, and PM2.5 using a Bayesian kernel machine regression approach.

BACKGROUND: Air pollution has many adverse health effects, but the combined or synergistic effects of multiple ambient air pollutants on anti-nuclear antibodies (ANA, a serologic marker of systemic autoimmune rheumatic disease, SARDs) have never been assessed. OBJECTIVE: To flexibly model ANA and individual and joint associations of long-term exposures to nitrogen dioxide (NO2), ozone (O3), and fine particles matter (PM2.5) using a Bayesian Kernel machine regression (BKMR) approach and to compare the results to those from individual logistic regressions. METHODS: Serum ANA positivity was determined for randomly selected CARTaGENE general population subjects in Quebec, Canada. CARTaGENE is a public research platform created for investigating the associations of environmental, genomic, and lifestyle factors on chronic diseases. Ambient NO2, O3, and PM2.5 estimates, derived from ground-measurement and chemical-transport-model simulated concentrations, were assigned to subjects based on residential postal codes at the time of blood collection. Our models adjusted for age, sex, French Canadian origin, smoking, and family income. RESULTS: Concentrations of NO2, O3, and PM2.5 were closely correlated in space. In the 5485 CARTaGENE subjects studied, we did not see clear associations between NO2, PM2.5 or O3 and ANA positivity, with either the BKMR or logistic models. CONCLUSIONS: BKMR did not uncover associations between ANA positivity and individual levels or combined exposures of NO2, O3, and PM2.5; neither did simpler logistic models. Additional studies (in younger populations, in distinct race/ethnicity groups, and/or in jurisdictions with high air pollution levels) would be helpful to reinforce current findings.

Revisiting the estimations of PM2.5-attributable mortality with advancements in PM2.5 mapping and mortality statistics.

With the advancements of geospatial technologies, geospatial datasets of fine particulate matter (PM2.5) and mortality statistics are increasingly used to examine the health effects of PM2.5. Choices of these datasets with difference geographic characteristics (e.g., accuracy, scales, and variations) in disease burden studies can significantly impact the results. The objective of this study is to revisit the estimations of PM2.5-attributable mortality by taking advantage of recent advancements in high resolution mapping of PM2.5concentrations and fine scale of mortality statistics and to explore the impacts of new data sources, geographic scales, and spatial variations of input datasets on mortality estimations. We estimate the PM2.5-mortality for the years of 2000, 2005, 2010 and 2015 using three PM2.5 concentration datasets [Chemical Transport Model (CTM), random forests-based regression kriging (RFRK), and geographically weighted regression (GWR)] at two resolutions (i.e., 10 km and 1 km) and mortality rates at two geographic scales (i.e., regional-level and county-level). The results show that the estimated PM2.5-mortality from the 10 km CTM-derived PM2.5 dataset tend to be smaller than the estimations from the 1 km RFRK- and GWR-derived PM2.5 datasets. The estimated PM2.5-mortalities from regional-level mortality rates are similar to the estimations from those at county level, while large deviations exist when zoomed into small geographic regions (e.g., county). In a scenario analysis to explore the possible benefits of PM2.5 concentrations reduction, the uses of the two newly developed 1 km resolution PM2.5 datasets (RFRK and GWR) lead to discrepant results. Furthermore, we found that the change in PM2.5 concentration is the primary factor that leads to the PM2.5-attributable mortality decrease from 2000 to 2015. The above results highlight the impact of the adoption of input datasets from new sources with varied geographic characteristics on the PM2.5-attributable mortality estimations and demonstrate the necessity to account for these impact in future disease burden studies. CAPSULE: We revisited the estimations of PM2.5-attributable mortality with advancements in PM2.5 mapping and mortality statistics, and demonstrated the impact of geographic characteristics of geospatial datasets on mortality estimations.

Improved population mapping for China using remotely sensed and points-of-interest data within a random forests model.

Remote sensing image products (e.g. brightness of nighttime lights and land cover/land use types) have been widely used to disaggregate census data to produce gridded population maps for large geographic areas. The advent of the geospatial big data revolution has created additional opportunities to map population distributions at fine resolutions with high accuracy. A considerable proportion of the geospatial data contains semantic information that indicates different categories of human activities occurring at exact geographic locations. Such information is often lacking in remote sensing data. In addition, the remarkable progress in machine learning provides toolkits for demographers to model complex nonlinear correlations between population and heterogeneous geographic covariates. In this study, a typical type of geospatial big data, points-of-interest (POIs), was combined with multi-source remote sensing data in a random forests model to disaggregate the 2010 county-level census population data to 100 × 100 m grids. Compared with the WorldPop population dataset, our population map showed higher accuracy. The root mean square error for population estimates in Beijing, Shanghai, Guangzhou, and Chongqing for this method and WorldPop were 27,829 and 34,193, respectively. The large under-allocation of the population in urban areas and over-allocation in rural areas in the WorldPop dataset was greatly reduced in this new population map. Apart from revealing the effectiveness of POIs in improving population mapping, this study promises the potential of geospatial big data for mapping other socioeconomic parameters in the future.

Improve ground-level PM2.5 concentration mapping using a random forests-based geostatistical approach.

Accurate measurements of ground-level PM2.5 (particulate matter with aerodynamic diameters equal to or less than 2.5 µm) concentrations are critically important to human and environmental health studies. In this regard, satellite-derived gridded PM2.5 datasets, particularly those datasets derived from chemical transport models (CTM), have demonstrated unique attractiveness in terms of their geographic and temporal coverage. The CTM-based approaches, however, often yield results with a coarse spatial resolution (typically at 0.1° of spatial resolution) and tend to ignore or simplify the impact of geographic and socioeconomic factors on PM2.5 concentrations. In this study, with a focus on the long-term PM2.5 distribution in the contiguous United States, we adopt a random forests-based geostatistical (regression kriging) approach to improve one of the most commonly used satellite-derived, gridded PM2.5 datasets with a refined spatial resolution (0.01°) and enhanced accuracy. By combining the random forests machine learning method and the kriging family of methods, the geostatistical approach effectively integrates ground-based PM2.5 measurements and related geographic variables while accounting for the non-linear interactions and the complex spatial dependence. The accuracy and advantages of the proposed approach are demonstrated by comparing the results with existing PM2.5 datasets. This manuscript also highlights the effectiveness of the geographical variables in long-term PM2.5 mapping, including brightness of nighttime lights, normalized difference vegetation index and elevation, and discusses the contribution of each of these variables to the spatial distribution of PM2.5 concentrations.

The effects of synoptic weather on influenza infection incidences: a retrospective study utilizing digital disease surveillance.

The environmental drivers and mechanisms of influenza dynamics remain unclear. The recent development of influenza surveillance--particularly the emergence of digital epidemiology--provides an opportunity to further understand this puzzle as an area within applied human biometeorology. This paper investigates the short-term weather effects on human influenza activity at a synoptic scale during cold seasons. Using 10 years (2005-2014) of municipal level influenza surveillance data (an adjustment of the Google Flu Trends estimation from the Centers for Disease Control's virologic surveillance data) and daily spatial synoptic classification weather types, we explore and compare the effects of weather exposure on the influenza infection incidences in 79 cities across the USA. We find that during the cold seasons the presence of the polar [i.e., dry polar (DP) and moist polar (MP)] weather types is significantly associated with increasing influenza likelihood in 62 and 68% of the studied cities, respectively, while the presence of tropical [i.e., dry tropical (DT) and moist tropical (MT)] weather types is associated with a significantly decreasing occurrence of influenza in 56 and 43% of the cities, respectively. The MP and the DP weather types exhibit similar close positive correlations with influenza infection incidences, indicating that both cold-dry and cold-moist air provide favorable conditions for the occurrence of influenza in the cold seasons. Additionally, when tropical weather types are present, the humid (MT) and the dry (DT) weather types have similar strong impacts to inhibit the occurrence of influenza. These findings suggest that temperature is a more dominating atmospheric factor than moisture that impacts the occurrences of influenza in cold seasons.

[Effects of multi-angle hyperspectral polarized reflection by forest soil].

In the present study, the authors measured samples of typical forest soils in different states with multi-angle hyperspectral polarized reflections. The authors analyzed multi-angle hyperspectral polarized reflections of soil data with various viewing zenith angles, incidence angles, relative azimuth angles, polarized states, soil water content and soil granule. The authors found that those factors affected the reflectance values of forest soils but not the spectral feature. The conclusions included that the larger the incidence angles and viewing zenith angles are, the bigger the polarized reflectance values of the surface of the forest soil. When the forest soil was dry, the surface had phenomenon of diffuse reflection and the polarized light reflection did not take place. When the soil moisture content reached a certain level, the polarized reflection appeared. The more the moisture content of the forest soil was, the smaller the polarized reflectance of the surface. The bigger the soil granule was and the rougher the soil surface was, the smaller the surface polarized reflectance. The results and conclusions suggested that the spectral characteristics of the ground target need to be considered adequately in order to design the best mode for sensor systems by remote sensing technology. The authors suggest that the incidence angle and viewing zenith angle be selected on the basis of factual instance. The authors suggest using larger viewing zenith angles and that the incidence angle should be equal to the viewing zenith angle. In the meantime, the effects of sheltering by ground targets need to be considered and the proper state of polarization should be chosen while keeping relative zenith angle at 180 degrees. This study not only helps find a new way for detection of soil characters, but also provides a theoretical basis for further research on multi-angle hyperspectral polarized reflection for detecting characteristic spectrum and best states in measuring forest soil.

[Study on the main influencing factors of mixed-pixel spectral characteristics].

Hyperspectral remote sensing can improve the identification and classification of surface features through the spectrum comparing and matching to achieve classification and recognition. Because of the spatial resolution of the sensor as well as the difference in complexity and diversity on the ground, mixed pixels in the image are prevalent in remote sensing. The problem of subpixel unmixing is a prominent issue in the quantitative application of remote sensing. How to effectively interpret the mixed-pixel is one of the key issues in the application of remote sensing. In the present paper, the hyperspectral reflectance characteristics of the mixed-pixels formed with two kinds of materials whose area ratios have always been 1 : 1 were studied at different incident zenith angles and different topology location distribution, which provides a theoretical basis for the mixed pixel classification accuracy improvement.