Association of weather and air pollution interactions on daily mortality in 12 Canadian cities

Effects of ambient temperature and humidity on COPD mortality in Ganzhou city, China

This study used the time series data of Ganzhou city to explore the individual and interaction effects of temperature and humidity on COPD death, and identify vulnerable subgroups of the population. We collected daily COPD mortality and meteorological data in Ganzhou from 2016 to 2019. The nonlinear distribution lag model was used to examine the associations and interaction between daily mean temperature and humidity and COPD mortality. For the total population, male and 65 years old or above, the relative risk (RR) for COPD mortality could be significant at extremely low temperature (3.3 ℃), reaching 1.799 (95% confidence interval [CI]: 1.216, 2.662), 1.894 (95% CI: 1.164, 3.084) and 1.779 (95% CI:1.185, 2.670). Also, at extremely low humidity (47.8%), the risk reached 1.888 (95% CI: 1.217, 2.930), 1.837 (95% CI: 1.066, 3.165) and 2.166 (95% CI: 1.375, 3.414). The cumulative COPD death risk for females was 3.524 (95% CI: 1.340, 9.267) at high temperature (30.7 ℃), 1.953(95% CI: 1.036, 3.683) at low humidity (47.8%) and 1.726 (95% CI: 1.048, 2.845) at high humidity (96.7%). For the total COPD deaths and subgroups, the interaction effects between daily temperature and humidity were not significant (p > 0.05). Both extremely low temperature and low humidity increased the risk of COPD death in Ganzhou city, especially for males and people over 65 years old. Females were more sensitive to extremely high temperature and humidity. Patients with COPD should pay attention to self-protection under extreme temperature and humidity weather conditions.

Concurrent Heat and Air Pollution Exposures among People Experiencing Homelessness

BACKGROUND

Extreme heat and air pollution are important human health concerns; exposure can affect mental and physical well-being, particularly during periods of co-occurrence. Yet, the impacts on people are largely determined by underlying health conditions, coupled with the length and intensity of exposure. Preexisting adverse health conditions and prolonged exposure times are more common for people experiencing homelessness, particularly those with intersectional identity characteristics (e.g., disease, ability, age, etc.). Partially due to methodological limitations, such as data scarcity, there is a lack of research at the intersection of this at-risk population within the climate-health domain.

OBJECTIVES

We have three distinct objectives throughout this article: a) to advance critical discussions around the state of concurrent high heat and air pollution exposure research as it relates to people experiencing homelessness; b) to assert the importance of heat and air pollution exposure research among a highly vulnerable, too-often homogenized population-people experiencing homelessness; and c) to underline challenges in this area of study while presenting potential ways to address such shortcomings.

DISCUSSION

The health insights from concurrent air pollution and heat exposure studies are consequential when studying unhoused communities who are already overexposed to harmful environmental conditions. Without holistic data sets and more advanced methods to study concurrent exposures, appropriate and targeted prevention and intervention strategies cannot be developed to protect this at-risk population. We highlight that a) concurrent high heat and air pollution exposure research among people experiencing homelessness is significantly underdeveloped considering the pressing human health implications; b) the severity of physiological responses elicited by high heat and air pollution are predicated on exposure intensity and time, and thus people without means of seeking climate-controlled shelter are most at risk; and c) collaboration among transdisciplinary teams is needed to resolve data resolution issues and enable targeted prevention and intervention strategies. https://doi.org/10.1289/EHP13402.

Projection of extreme heat- and cold-related mortality in Sweden based on the spatial synoptic classification

BACKGROUND

Climate change is projected to result in increased heat events and decreased cold events. This will substantially impact human health, particularly when compounded with demographic change. This study employed the Spatial Synoptic Classification (SSC) to categorize daily weather into one of seven types. Here we estimated future mortality due to extremely hot and cold weather types under different climate change scenarios for one southern (Stockholm) and one northern (Jämtland) Swedish region.

METHODS

Time-series Poisson regression with distributed lags was used to assess the relationship between extremely hot and cold weather events and daily deaths in the population above 65 years, with cumulative effects (6 days in summer, 28 days in winter), 1991 to 2014. A global climate model (MPI-M-MPI-ESM-LR) and two climate change scenarios (RCP 4.5 and 8.5) were used to project the occurrence of hot and cold days from 2031 to 2070. Place-specific projected mortality was calculated to derive attributable numbers and attributable fractions (AF) of heat- and cold-related deaths.

RESULTS

In Stockholm, for the RCP 4.5 scenario, the mean number of annual deaths attributed to heat increased from 48.7 (CI 32.2-64.2; AF = 0.68%) in 2031-2040 to 90.2 (56.7-120.5; AF = 0.97%) in 2061-2070, respectively. For RCP 8.5, heat-related deaths increased more drastically from 52.1 (33.6-69.7; AF = 0.72%) to 126.4 (68.7-175.8; AF = 1.36%) between the first and the last decade. Cold-related deaths slightly increased over the projected period in both scenarios. In Jämtland, projections showed a small decrease in cold-related deaths but no change in heat-related mortality.

CONCLUSIONS

In rural northern region of Sweden, a decrease of cold-related deaths represents the dominant trend. In urban southern locations, on the other hand, an increase of heat-related mortality is to be expected. With an increasing elderly population, heat-related mortality will outweigh cold-related mortality at least under the RCP 8.5 scenario, requiring societal adaptation measures.

Impacts of weather and air pollution on Legionnaires' disease in Switzerland: A national case-crossover study

BACKGROUND

The number of reported cases of Legionnaires' disease (LD) has risen markedly in Switzerland (6.5/100,000 inhabitants in 2021) and abroad over the last decade. Legionella, the causative agent of LD, are ubiquitous in the environment. Therefore, environmental changes can affect the incidence of LD, for example by increasing bacterial concentrations in the environment or by facilitating transmission.

OBJECTIVES

The aim of this study is to understand the environmental determinants, in particular weather conditions, for the regional and seasonal distribution of LD in Switzerland.

METHODS

We conducted a series of analyses based on the Swiss LD notification data from 2017 to 2021. First, we used a descriptive and hotspot analysis to map LD cases and identify regional clusters. Second, we applied an ecological model to identify environmental determinants on case frequency at the district level. Third, we applied a case-crossover design using distributed lag non-linear models to identify short-term associations between seven weather variables and LD occurrence. Lastly, we performed a sensitivity analysis for the case-crossover design including NO2 levels available for the year 2019.

RESULTS

Canton Ticino in southern Switzerland was identified as a hotspot in the cluster analysis, with a standardised notification rate of 14.3 cases/100,000 inhabitants (CI: 12.6, 16.0). The strongest association with LD frequency in the ecological model was found for large-scale factors such as weather and air pollution. The case-crossover study confirmed the strong association of elevated daily mean temperature (OR 2.83; CI: 1.70, 4.70) and mean daily vapour pressure (OR: 1.52, CI: 1.15, 2.01) 6-14 days before LD occurrence.

DISCUSSION

Our analyses showed an influence of weather with a specific temporal pattern before the onset of LD, which may provide insights into the effect mechanism. The relationship between air pollution and LD and the interplay with weather should be further investigated.

Association between averaged meteorological factors and tuberculosis risk: A systematic review and meta-analysis

Inconsistencies were discovered in the findings regarding the effects of meteorological factors on tuberculosis (TB). This study conducted a systematic review of published studies on the relationship between TB and meteorological factors and used a meta-analysis to investigate the pooled effects in order to provide evidence for future research and policymakers. The literature search was completed by August 3rd, 2021, using three databases: PubMed, Web of Science and Embase. Relative risks (RRs) in included studies were extracted and all effect estimates were combined together using meta-analysis. Subgroup analyses were carried out based on the resolution of exposure time, regional climate, and national income level. A total of eight studies were included after screening for inclusion and exclusion criteria. Our results show that TB risk was positively correlated with precipitation (RR = 1.32, 95% CI: 1.14, 1.51), while temperature (RR = 1.15, 95% CI: 1.00, 1.32), humidity (RR = 1.05, 95% CI: 0.99, 1.10), air pressure (RR = 0.89, 95% CI: 0.69, 1.14) and sunshine duration (RR = 0.95, 95% CI: 0.80, 1.13) all had no statistically significant correlation. Subgroup analysis shows that quarterly measure resolution, low and middle Human Development Index (HDI) level and subtropical climate increase TB risk not only in precipitation, but also in temperature and humidity. Moreover, less heterogeneity was observed in "high and extremely high" HDI areas and subtropical areas than that in other subgroups (I² = 0%). Precipitation, a subtropical climate, and a low HDI level are all positive influence factors to tuberculosis. Therefore, residents and public health managers should take precautionary measures ahead of time, especially in extreme weather conditions.

Cows as canaries: The effects of ambient air pollution exposure on milk production and somatic cell count in dairy cows

Exposure to air pollution, including criteria pollutants such as fine particulate matter (PM_2.5) and ozone (O₃), has been associated with morbidity and mortality in mammals. As a genetically homogenous population that is closely monitored for health, dairy cattle present a unique opportunity to assess the association between changes in air pollution and mammalian health. Milk yield decreases in the summer if temperature and humidity, measured by the Temperature Humidity Index (THI). As O₃ levels increase with warmer temperatures, and summer PM_2.5 may increase with wildfire smoke, dairy cows may serve as a useful sentinel species to evaluate subacute markers of inflammation and metabolic output and ambient pollution. Over two years, we assessed summertime O₃ and PM_2.5 concentrations from local US EPA air quality monitors into an auto-regressive mixed model of the association between THI and daily milk production data and bulk tank somatic cell count (SCC). In unadjusted models, a 10 unit increase THI was associated with 28,700 cells/mL (95% CI: 17,700, 39,690) increase in SCC. After controlling for ambient air pollutants, THI was associated with a 14,500 SCC increase (95% CI: 3,400, 25,680), a 48% decrease in effect compared to the crude model. Further, in fully adjusted models, PM_2.5 was associated with a 105,500 cells/mL (95% CI: 90,030, 121,050) increase in SCC. Similar results were found for milk production. Results were amplified when high PM_2.5 days (95th percentile of observed values) associated with wildfire smoke were removed from the analyses. Our results support the hypothesis that PM_2.5 confounds the relationships between THI and milk yield and somatic cell count. The results of this study can be used to inform strategies for intervention to mitigate these impacts at the dairy level and potentially contribute to a model where production animals can act as air quality sentinels.

Association between Ambient Air Pollutants and Pneumonia in Wuhan, China, 2014–2017

Objectives: To assess associations between short-time air pollution exposure and outpatient visits for pneumonia by the distributed lag nonlinear model (DLNM). Methods: Daily outpatient visits for pneumonia and air pollutant data were collected from Wuhan Basic Medical Insurance Database in China and 10 national air quality monitoring stations in Wuhan from 2014 to 2017, respectively. Taking the first percentile of the concentration as the reference, DLNM was used to estimate the impact of moderate (50th) and high levels (99th) of pollutants on pneumonia. Results: A total of 133,882 outpatient visits were identified during the period of the study. Moderate-level (P50) fine particulate matter (PM2.5) or sulfur dioxide (SO2) and high-level nitrogen dioxide (NO2) (P99) can increase the risk of pneumonia. The maximum RR was 1.198 (95% CI: 1.094–1.311) at lag0-11, 1.304 (95% CI: 1.166–1.458) at lag0-13, and 1.286 (95% CI: 1.060–1.561) at lag0-14, respectively. Females and children had greater risks. Conclusions: Short-time PM2.5, SO2, and NO2 exposure were associated with outpatient visits for pneumonia in Wuhan, China.

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.

Exploring rural hospital admissions for diarrhoeal disease, malaria, pneumonia, and asthma in relation to temperature, rainfall and air pollution using wavelet transform analysis

BACKGROUND

Climate variables impact human health and in an era of climate change, there is a pressing need to understand these relationships to best inform how such impacts are likely to change.

OBJECTIVES

This study sought to investigate time series of daily admissions from two public hospitals in Limpopo province in South Africa with climate variability and air quality.

METHODS

We used wavelet transform cross-correlation analysis to monitor coincidences in changes of meteorological (temperature and rainfall) and air quality (concentrations of PM_2.5 and NO₂) variables with admissions to hospitals for gastrointestinal illnesses including diarrhoea, pneumonia-related diagnosis, malaria and asthma cases. We were interested to disentangle meteorological or environmental variables that might be associated with underlying temporal variations of disease prevalence measured through visits to hospitals.

RESULTS

We found preconditioning of prevalence of pneumonia by changes in air quality and showed that malaria in South Africa is a multivariate event, initiated by co-occurrence of heat and rainfall. We provided new statistical estimates of time delays between the change of weather or air pollution and increase of hospital admissions for pneumonia and malaria that are addition to already known seasonal variations. We found that increase of prevalence of pneumonia follows changes in air quality after a time period of 10 to 15 days, while the increase of incidence of malaria follows the co-occurrence of high temperature and rainfall after a 30-day interval.

DISCUSSION

Our findings have relevance for early warning system development and climate change adaptation planning to protect human health and well-being.

Characterization of low-dose ozone-induced murine acute lung injury

Ozone is a toxic and highly reactive gaseous oxidizing chemical with well‐documented adverse health effects in humans. On the basis of animal and human data, environmental guidelines and air quality standards recommend a threshold for exposure of no more than 0.063 ppm of ozone (daily concentrations). This research describes a standardized sensitive model of sterile murine lung inflammation induced by exposing mice to acute (0, 4 or 24 hr), yet low, levels of ozone (0.005, 0.05 or 0.5 ppm), one that are below the current recommendations for what is considered a safe or “ambient” ozone concentration for humans. Ozone led to concentration and time‐dependent phlogistic cell death in the bronchoalveolar lavage, lung epithelial damage and hemorrhage. Interestingly, we observed distinct large bright CD11b positive cells in the bronchoalveolar lavage, upregulation of lung vascular and alveolar ATP synthase as well as plasminogen and bronchiolar angiostatin expression in ozone‐exposed mice, platelet and neutrophil accumulation in the lung vasculature and an eotaxin‐2, IL‐16, CXCL5, CXCL12, and CXCL13 dominant inflammatory response leading to lung injury. Using a fluorescent intravital microscopy set up, we quantified ozone‐induced extensive alveolar cellular damage. We observed ozone‐induced actin filament disorganization, perturbed respiratory mechanics, acute suppression of the alveolar reactive oxygen species (ROS) production and mitochondrial potential in ventilated lungs. We present evidence of systemic, as well as pulmonary toxicity, at 40‐fold lower ozone concentrations than previously reported in mice. The findings are important in establishing a sensitive means of quantifying structural and functional lung disorganization following exposure to an aerosolized pollutant, even at levels of ozone exposure previously thought to be safe in humans.

Aerosols optical depth and Ångström exponent over different regions in Garhwal Himalaya, India

Aerosol optical depth (AOD) and Ångström exponent (AE) are observed to be important parameters in understanding the status of ambient aerosol concentration over a particular location and depend not only upon the local but also on the large-scale dynamics of the atmosphere. The present article analyses the AOD and AE parameters retrieved with Moderate Resolution Imaging Spectrometer (MODIS) and Multi-angle Imaging Spectro-Radiometer (MISR) instruments onboard satellites, for the upper (Chamoli) and foothill (Dehradun) regions of Garhwal Himalaya in Uttarakhand, India, from 2006 to 2015. Aerosol properties are investigated at monthly, seasonal, and annual scales. The monthly mean values of MODIS-derived AOD and AE were observed to be 0.18 (± 0.14) and 1.05 (± 0.43) respectively over the Dehradun region. The seasonal maximums in AOD with MODIS and MISR were observed as 0.23 ± 0.06 and 0.29 ± 0.07 respectively in the pre-monsoon season, and the minimum values (0.099 ± 0.02) were observed in the post-monsoon season, over the Dehradun region. In contrast, in the Chamoli region, the maximum AOD (MODIS) was 0.21 ± 0.06 observed in the monsoon season and the minimum was 0.036 ± 0.007 in the post-monsoon season. Over a decade, the AE for Chamoli and Dehradun was found to vary from 0.07 to 0.17 and from 0.14 to 0.20 respectively. The median AE for Chamoli and Dehradun was found to be 1.49 and 1.47 respectively, marking the dominance of fine mode particles of anthropogenic origin. Observations show the presence of dust and polluted dust resulting from the long-range transport from the west. The comparison of AOD values from the two sensors shows a significant correlation (0.73) with slightly higher values from MISR over the year. The results obtained are important in understanding the climatic implications due to the atmospheric aerosols over the abovementioned Himalayan region of Uttarakhand, India.

Prospective correlational time-series analysis of the influence of weather and air pollution on joint pain in chronic rheumatic diseases

OBJECTIVES

The primary objective was to evaluate the association between weather variables and joint pain in patients with chronic rheumatic diseases (CRD: rheumatoid arthritis (RA), osteoarthritis (OA), and spondyloarthritis (SpA)). A secondary objective was to study the impact of air pollution indicators on CRD pain.

METHOD

The study is prospective, correlational, with time-series analysis. Patients with CRD, living in a predefined catchment area, filled their level of pain daily using a 0-10 numerical scale (NS), for 1 year. Weather (temperature, relative humidity (H), atmospheric pressure (P)) and air pollution indicators (particulate matters (PM₁₀, PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃)) were recorded daily using monitoring systems positioned in the same area. Association between pain and weather and air pollution indicators was studied using Pearson's correlation. Time-series analysis methodology was applied to determine the temporal relationship between pain and indicators.

RESULTS

The study included 94 patients, 82% reported they were weather-sensitive. Pain variation was similar across diseases over a year. Pain was associated negatively with temperature, H, and O_3, and positively with P and NO₂. However, the strength of correlation was moderate; temperature explained 22% of pain variance. A drop of 10°C in temperature corresponded to an increase of 0.5 points in pain NS. Also, there was a significant interaction among environmental factors. In time-series analysis, temperature and NO₂ remained independently associated with pain.

CONCLUSIONS

The perception of joint pain in patients with CRD was correlated with weather and air pollution. The strength of association was moderate and independent of underlying disease. Key Points •Weather variation was moderately correlated with joint pain in chronic rheumatic diseases, with an inverse association with temperature, humidity, and O₃. • Air pollution indicators, mainly nitrogen dioxide and ozone, were correlated with joint pain; particulate matters were also correlated but to a lesser extent. • The influence of these environmental factors was independent of the type of rheumatic disease, thus raising the hypothesis of their impact on pain perception mechanisms.

Synergistic health effects of air pollution, temperature, and pollen exposure: a systematic review of epidemiological evidence

BACKGROUND

Exposure to heat, air pollution, and pollen are associated with health outcomes, including cardiovascular and respiratory disease. Studies assessing the health impacts of climate change have considered increased exposure to these risk factors separately, though they may be increasing simultaneously for some populations and may act synergistically on health. Our objective is to systematically review epidemiological evidence for interactive effects of multiple exposures to heat, air pollution, and pollen on human health.

METHODS

We systematically searched electronic literature databases (last search, April 29, 2019) for studies reporting quantitative measurements of associations between at least two of the exposures and mortality from any cause and cardiovascular and respiratory morbidity and mortality specifically. Following the Navigation Guide systematic review methodology, we evaluated the risk of bias of individual studies and the overall quality and strength of evidence.

RESULTS

We found 56 studies that met the inclusion criteria. Of these, six measured air pollution, heat, and pollen; 39 measured air pollution and heat; 10 measured air pollution and pollen; and one measured heat and pollen. Nearly all studies were at risk of bias from exposure assessment error. However, consistent exposure-response across studies led us to conclude that there is overall moderate quality and sufficient evidence for synergistic effects of heat and air pollution. We concluded that there is overall low quality and limited evidence for synergistic effects from simultaneous exposure to (1) air pollution, pollen, and heat; and (2) air pollution and pollen. With only one study, we were unable to assess the evidence for synergistic effects of heat and pollen.

CONCLUSIONS

If synergistic effects between heat and air pollution are confirmed with additional research, the health impacts from climate change-driven increases in air pollution and heat exposure may be larger than previously estimated in studies that consider these risk factors individually.

Lag Variables in Nitrogen Oxide Concentration Modelling: A Case Study in Wrocław, Poland

Due to the unwavering interest of both residents and authorities in the air quality of urban agglomerations, we pose the following question in this paper: What impact do current and past meteorological factors and traffic flow intensity have on air quality? What is the impact of lagged variables on the fit of an explanation model, and how do they affect its ability to predict? We focused on NO2 and NOx concentrations, and conducted this research using hourly data from the city of Wrocław (western Poland) from 2015 to 2017; we used multi-objective optimization to determine the optimal delays. It turned out that for both NO2 and NOx, the past values for traffic flow, wind speed, and sunshine duration are more important than the current ones. We built random forest models on each of the pollutants for both the current and past values and discovered that including a lagged variable increases the resulting R2 from 0.51 to 0.56 for NO2 and from 0.46 to 0.52 for NOx. We also analyzed the feature importance in each model, and found that for NO2, a wind speed delay of more than three hours causes a significant decrease, while the importance of relative humidity increases with a seven-hour delay; likewise, wind speed increases the importance for NOx prediction with a two-hour delay. We concluded that, in pollutant concentration modeling, the possibility of a delayed effect of the independent variables should always be considered, because it can significantly increase the performance of the model and suggest unexpected relationships or dependencies.

Major air pollutants seasonal variation analysis and long-range transport of PM10 in an urban environment with specific climate condition in Transylvania (Romania)

The air quality decrease, especially in urban areas, is related to local-scale conditions and to dispersion of air pollutants (regional and long-range) as well. The main objective of this study was to decipher the seasonal variation of PM10, NO, NO2, NOx, SO2, O3, and CO over a 1-year period (2017) and the possible relationships between air pollution and meteorological variables. Furthermore, trajectory cluster analysis and concentration-weighted trajectory (CWT) methods were used to assess the trajectories and the source-receptor relationship of PM10 in the Ciuc basin Transylvania, known as the "Cold Pole" of Romania. The pollutants show lower concentrations during warmer periods, especially during summer, and significantly higher concentrations were observed on heating season in winter due to seasonal variations in energy use (biomass burning) and atmospheric stability. Subsequently, in February, the highest concentration of PM10 was 132 μg/m3, which is 4 times higher than the highest recorded monthly mean. Our results indicate a negative correlation between CO/temperature (- 0.89), NOx/temperature (- 0.84) and positive between NOx/PM10 (0.95), CO/PM10 (0.9), and NOx/CO (0.98), respectively. Dominant transport pathways were identified and the results revealed that slow-moving southerly (~ 45%) and northwesterly (~ 32%) air masses represent almost 80% and mainly regional flows were discerned. During 2017, increased PM10 levels were measured at the study site when air masses arrived mostly from northwest and southeast. The CWT and polarplot models show a strong seasonal variation and significant differences were observed between weekdays and weekends, namely highest PM10 concentrations during weekends at low wind speed (2-4 m/s).

A Comparative Analysis for Air Quality Estimation from Traffic and Meteorological Data

Air pollution in urban regions remains a crucial subject of study, given its implications on health and environment, where much effort is often put into monitoring pollutants and producing accurate trend estimates over time, employing expensive tools and sensors. In this work, we study the problem of air quality estimation in the urban area of Milan (IT), proposing different machine learning approaches that combine meteorological and transit-related features to produce affordable estimates without introducing sensor measurements into the computation. We investigated different configurations employing machine and deep learning models, namely a linear regressor, an Artificial Neural Network using Bayesian regularization, a Random Forest regressor and a Long Short Term Memory network. Our experiments show that affordable estimation results over the pollutants can be achieved even with simpler linear models, therefore suggesting that reasonably accurate Air Quality Index (AQI) measurements can be obtained without the need for expensive equipment.

Delay effect and burden of weather-related tuberculosis cases in Rajshahi province, Bangladesh, 2007-2012

Tuberculosis (TB) is a potentially fatal infectious disease that continues to be a public health problem in Bangladesh. Each year in Bangladesh an estimated 70,000 people die of TB and 300,000 new cases are projected. It is important to understand the association between TB incidence and weather factors in Bangladesh in order to develop proper intervention programs. In this study, we examine the delayed effect of weather variables on TB occurrence and estimate the burden of the disease that can be attributed to weather factors. We used generalized linear Poisson regression models to investigate the association between weather factors and TB cases reported to the Bangladesh National TB control program between 2007 and 2012 in three known endemic districts of North-East Bangladesh. The associated risk of TB in the three districts increases with prolonged exposure to temperature and rainfall, and persisted at lag periods beyond 6 quarters. The association between humidity and TB is strong and immediate at low humidity, but the risk decreases with increasing lag. Using the optimum weather values corresponding to the lowest risk of infection, the risk of TB is highest at low temperature, low humidity and low rainfall. Measures of the risk attributable to weather variables revealed that weather-TB cases attributed to humidity is higher than that of temperature and rainfall in each of the three districts. Our results highlight the high linearity of temporal lagged effects and magnitudes of the burden attributable to temperature, humidity, and rainfall on TB endemics. The results can hopefully advise the Bangladesh National TB control program and act as a practical reference for the early warning of TB cases.

Short-term exposure to sulfur dioxide and the risk of childhood hand, foot, and mouth disease during different seasons in Hefei, China

BACKGROUND

Sulfur dioxide (SO₂) is an important component of air pollution, adversely impacting human health worldwide. This study aimed to examine the association between short-term exposure to SO₂ and childhood hand, foot, and mouth disease (HFMD) in Hefei, China.

METHODS

A Poisson generalized additive model (GAM) combining the time-series regression analyses was used to fit the SO₂-HFMD association. The effect of SO₂ was estimated using the single-day lag models (lag0, lag1, lag2) and the moving average lag models (lag01, lag02) We also conducted stratified analyses by season, ages (0-4 years old, 5-14 years old), gender (male, female), childcare patterns (scattered children, kindergarten children) and residence areas (urban, rural). Two-pollutant models were adopted to test the robustness of the results.

RESULTS

There was a statistically significant association between SO₂ and the risk of childhood HFMD. For total cases, the relative risk (RR) at lag0 was 1.038 (95% confidence interval (CI): 1.018-1.057) in whole-period and 1.088 (95% CI: 1.059-1.118) in cold season. During cold season, we observed significant associations between SO₂ and HFMD among all subgroups except for children aged 5-14 years old and the adverse effects occurred on lag0, lag1, lag01, lag02. However, in hot season, SO₂ were significant only for females (lag01 with RR = 1.054; 95%CI = 1.007-1.101) and scattered children (lag01 with RR = 1.054; 95%CI = 1.007-1.101). In general, females and scattered children appeared to be more vulnerable to SO₂.

CONCLUSIONS

This study suggests a significant association between SO₂ and HFMD. especially during cold season. Compared with males and kindergarten children, females and scattered children are at higher risk of developing HFMD.

A new look at weather-related health impacts through functional regression

A major challenge of climate change adaptation is to assess the effect of changing weather on human health. In spite of an increasing literature on the weather-related health subject, many aspect of the relationship are not known, limiting the predictive power of epidemiologic models. The present paper proposes new models to improve the performances of the currently used ones. The proposed models are based on functional data analysis (FDA), a statistical framework dealing with continuous curves instead of scalar time series. The models are applied to the temperature-related cardiovascular mortality issue in Montreal. By making use of the whole information available, the proposed models improve the prediction of cardiovascular mortality according to temperature. In addition, results shed new lights on the relationship by quantifying physiological adaptation effects. These results, not found with classical model, illustrate the potential of FDA approaches.

Associations between short-term exposure to ambient sulfur dioxide and increased cause-specific mortality in 272 Chinese cities

BACKGROUND

Ambient sulfur dioxide (SO₂) remains a major air pollutant in developing countries, but epidemiological evidence about its health effects was not abundant and inconsistent.

OBJECTIVES

To evaluate the associations between short-term exposure to SO₂ and cause-specific mortality in China.

METHODS

We conducted a nationwide time-series analysis in 272 major Chinese cities (2013-2015). We used the over-dispersed generalized linear model together with the Bayesian hierarchical model to analyze the data. Two-pollutant models were fitted to test the robustness of the associations. We conducted stratification analyses to examine potential effect modifications by age, sex and educational level.

RESULTS

On average, the annual-mean SO₂ concentrations was 29.8 μg/m³ in 272 cities. We observed positive and associations of SO₂ with total and cardiorespiratory mortality. A 10 μg/m³ increase in two-day average concentrations of SO₂ was associated with increments of 0.59% in mortality from total non-accidental causes, 0.70% from total cardiovascular diseases, 0.55% from total respiratory diseases, 0.64% from hypertension disease, 0.65% from coronary heart disease, 0.58% from stroke, and 0.69% from chronic obstructive pulmonary disease. In two-pollutant models, there were no significant differences between single-pollutant model and two-pollutant model estimates with fine particulate matter, carbon monoxide and ozone, but the estimates decreased substantially after adjusting for nitrogen dioxide, especially in South China. The associations were stronger in warmer cities, in older people and in less-educated subgroups.

CONCLUSIONS

This nationwide study demonstrated associations of daily SO₂ concentrations with increased total and cardiorespiratory mortality, but the associations might not be independent from NO₂.

Aggregating the response in time series regression models, applied to weather-related cardiovascular mortality

In environmental epidemiology studies, health response data (e.g. hospitalization or mortality) are often noisy because of hospital organization and other social factors. The noise in the data can hide the true signal related to the exposure. The signal can be unveiled by performing a temporal aggregation on health data and then using it as the response in regression analysis. From aggregated series, a general methodology is introduced to account for the particularities of an aggregated response in a regression setting. This methodology can be used with usually applied regression models in weather-related health studies, such as generalized additive models (GAM) and distributed lag nonlinear models (DLNM). In particular, the residuals are modelled using an autoregressive-moving average (ARMA) model to account for the temporal dependence. The proposed methodology is illustrated by modelling the influence of temperature on cardiovascular mortality in Canada. A comparison with classical DLNMs is provided and several aggregation methods are compared. Results show that there is an increase in the fit quality when the response is aggregated, and that the estimated relationship focuses more on the outcome over several days than the classical DLNM. More precisely, among various investigated aggregation schemes, it was found that an aggregation with an asymmetric Epanechnikov kernel is more suited for studying the temperature-mortality relationship.

Benzo(a)pyrene parallel measurements in PM1 and PM2.5 in the coastal zone of the Gulf of Gdansk (Baltic Sea) in the heating and non-heating seasons

Parallel measurements of PM₁ and PM_2.5 aerosols were conducted in the urbanized coastal zone of the southern Baltic Sea. The main aim of the research was to assess and determine annual, seasonal (heating and non-heating), and daily concentration variability of benzo(a)pyrene in aerosols, these being the most dangerous constituents to human health. The average annual concentration of benzo(a)pyrene (B(a)P) was equal to 2.6 ng·m^-3 in PM₁ and 4.6 ng·m^-3 in PM_2.5, and both values were several times higher than the level of 1 ng·m^-3 which was set out in the CAFE Directive. High mean daily concentrations of B(a)P persisted for 50 and 65% of the study period in PM1 and PM2.5, respectively. In order to determine the sources of B(a)P in both aerosol fractions, organic (OC) and elemental (EC) carbon concentrations were examined. The highest concentrations of all carbon species were reported during the heating season under local or regional land advection and at low air temperatures. The origin of pollutants was the same and was primarily related to the combustion of fossil fuels in the communal-utility sector. During the non-heating period, the role of transportation, both land and marine, increased and may have been significant in creating higher concentrations of carbon compounds in PM₁ and PM_2.5. Regardless of the size of the aerosol fractions, B(a)P loads introduced into the Baltic coastal zone were several times higher during the heating period compared to the non-heating season. Graphical abstract ᅟ.

Heat/mortality sensitivities in Los Angeles during winter: a unique phenomenon in the United States

BACKGROUND

Extreme heat is often associated with elevated levels of human mortality, particularly across the mid-latitudes. Los Angeles, CA exhibits a unique, highly variable winter climate, with brief periods of intense heat caused by downsloping winds commonly known as Santa Ana winds. The goal is to determine if Los Angeles County is susceptible to heat-related mortality during the winter season. This is the first study to specifically evaluate heat-related mortality during the winter for a U.S. city.

METHODS

Utilizing the Spatial Synoptic Classification system in Los Angeles County from 1979 through 2010, we first relate daily human mortality to synoptic air mass type during the winter season (December, January, February) using Welch's t-tests. However, this methodology is only somewhat effective at controlling for important inter- and intra-annual trends in human mortality unrelated to heat such as influenza outbreaks. As a result, we use distributed lag nonlinear modeling (DLNM) to evaluate if the relative risk of human mortality increases during higher temperatures in Los Angeles, as the DLNM is more effective at controlling for variability at multiple temporal scales within the human mortality dataset.

RESULTS

Significantly higher human mortality is uncovered in winter when dry tropical air is present in Los Angeles, particularly among those 65 years and older (p < 0.001). The DLNM reveals the relative risk of human mortality increases when above average temperatures are present. Results are especially pronounced for maximum and mean temperatures, along with total mortality and those 65 + .

CONCLUSIONS

The discovery of heat-related mortality in winter is a unique finding in the United States, and we recommend stakeholders consider warning and intervention techniques to mitigate the role of winter heat on human health in the County.

Associations between long-term PM2.5 and ozone exposure and mortality in the Canadian Census Health and Environment Cohort (CANCHEC), by spatial synoptic classification zone

Studies suggest that long-term chronic exposure to fine particulate matter air pollution can increase lung cancer mortality. We analyzed the association between long term PM_2.5 and ozone exposure and mortality due to lung cancer, ischemic heart disease, and chronic obstructive pulmonary disease, accounting for geographic location, socioeconomic status, and residential mobility. Subjects in the 1991 Canadian Census Health and Environment Cohort (CanCHEC) were followed for 20years, and assigned to regions across Canada based on spatial synoptic classification weather types. Hazard ratios (HR) for mortality, were related to PM_2.5 and ozone using Cox proportional hazards survival models, adjusting for socioeconomic characteristics and individual confounders. An increase of 10μg/m³ in long term PM_2.5 exposure resulted in an HR for lung cancer mortality of 1.26 (95% CI 1.04, 1.53); the inclusion in the model of SSC zone as a stratum increased the risk estimate to HR 1.29 (95% CI 1.06, 1.57). After adjusting for ozone, HRs increased to 1.49 (95% CI 1.23, 1.88), and HR 1.54 (95% CI 1.27, 1.87), with and without zone as a model stratum. HRs for ischemic heart disease fell from 1.25 (95% CI 1.21, 1.29) for exposure to PM_2.5, to 1.13 (95% CI 1.08, 1.19) when PM_2.5 was adjusted for ozone. For COPD, the 95% confidence limits included 1.0 when climate zone was included in the model. HRs for all causes of death showed spatial differences when compared to zone 3, the most populated climate zone. Exposure to PM_2.5 was related to an increased risk of mortality from lung cancer, and both ozone and PM_2.5 exposure were related to risk of mortality from ischemic heart disease, and the risk varied spatially by climate zone.

Short-term effects of air quality and thermal stress on non-accidental morbidity-a multivariate meta-analysis comparing indices to single measures

Air quality and thermal stress lead to increased morbidity and mortality. Studies on morbidity and the combined impact of air pollution and thermal stress are still rare. To analyse the correlations between air quality, thermal stress and morbidity, we used a two-stage meta-analysis approach, consisting of a Poisson regression model combined with distributed lag non-linear models (DLNMs) and a meta-analysis investigating whether latitude or the number of inhabitants significantly influence the correlations. We used air pollution, meteorological and hospital admission data from 28 administrative districts along a north-south gradient in western Germany from 2001 to 2011. We compared the performance of the single measure particulate matter (PM10) and air temperature to air quality indices (MPI and CAQI) and the biometeorological index UTCI. Based on the Akaike information criterion (AIC), it can be shown that using air quality indices instead of single measures increases the model strength. However, using the UTCI in the model does not give additional information compared to mean air temperature. Interaction between the 3-day average of air quality (max PM10, max CAQI and max MPI) and meteorology (mean air temperature and mean UTCI) did not improve the models. Using the mean air temperature, we found immediate effects of heat stress (RR 1.0013, 95% CI: 0.9983-1.0043) and by 3 days delayed effects of cold stress (RR: 1.0184, 95% CI: 1.0117-1.0252). The results for air quality differ between both air quality indices and PM10. CAQI and MPI show a delayed impact on morbidity with a maximum RR after 2 days (MPI 1.0058, 95% CI: 1.0013-1.0102; CAQI 1.0068, 95% CI: 1.0030-1.0107). Latitude was identified as a significant meta-variable, whereas the number of inhabitants was not significant in the model.

Application of spatial synoptic classification in evaluating links between heat stress and cardiovascular mortality and morbidity in Prague, Czech Republic

Spatial synoptic classification (SSC) is here first employed in assessing heat-related mortality and morbidity in Central Europe. It is applied for examining links between weather patterns and cardiovascular (CVD) mortality and morbidity in an extended summer season (16 May-15 September) during 1994-2009. As in previous studies, two SSC air masses (AMs)-dry tropical (DT) and moist tropical (MT)-are associated with significant excess CVD mortality in Prague, while effects on CVD hospital admissions are small and insignificant. Excess mortality for ischaemic heart diseases is more strongly associated with DT, while MT has adverse effect especially on cerebrovascular mortality. Links between the oppressive AMs and excess mortality relate also to conditions on previous days, as DT and MT occur in typical sequences. The highest CVD mortality deviations are found 1 day after a hot spell's onset, when temperature as well as frequency of the oppressive AMs are highest. Following this peak is typically DT- to MT-like weather transition, characterized by decrease in temperature and increase in humidity. The transition between upward (DT) and downward (MT) phases is associated with the largest excess CVD mortality, and the change contributes to the increased and more lagged effects on cerebrovascular mortality. The study highlights the importance of critically evaluating SSC's applicability and benefits within warning systems relative to other synoptic and epidemiological approaches. Only a subset of days with the oppressive AMs is associated with excess mortality, and regression models accounting for possible meteorological and other factors explain little of the mortality variance.

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.

Relationship between fine particulate matter, weather condition and daily non-accidental mortality in Shanghai, China: A Bayesian approach

There are concerns that the reported association of ambient fine particulate matter (PM_2.5) with mortality might be a mixture of PM_2.5 and weather conditions. We evaluated the effects of extreme weather conditions and weather types on mortality as well as their interactions with PM_2.5 concentrations in a time series study. Daily non-accidental deaths, individual demographic information, daily average PM_2.5 concentrations and meteorological data between 2012 and 2014 were obtained from Shanghai, China. Days with extreme weather conditions were identified. Six synoptic weather types (SWTs) were generated. The generalized additive model was set up to link the mortality with PM_2.5 and weather conditions. Parameter estimation was based on Bayesian methods using both the Jeffreys’ prior and an informative normal prior in a sensitivity analysis. We estimate the percent increase in non-accidental mortality per 10 μg/m³ increase in PM_2.5 concentration and constructed corresponding 95% credible interval (CrI). In total, 336,379 non-accidental deaths occurred during the study period. Average daily deaths were 307. The results indicated that per 10 μg/m³ increase in daily average PM_2.5 concentration alone corresponded to 0.26–0.35% increase in daily non-accidental mortality in Shanghai. Statistically significant positive associations between PM_2.5 and mortality were found for favorable SWTs when considering the interaction between PM_2.5 and SWTs. The greatest effect was found in hot dry SWT (percent increase = 1.28, 95% CrI: 0.72, 1.83), followed by warm humid SWT (percent increase = 0.64, 95% CrI: 0.15, 1.13). The effect of PM_2.5 on non-accidental mortality differed under specific extreme weather conditions and SWTs. Environmental policies and actions should take into account the interrelationship between the two hazardous exposures.

Factors determining dry deposition of total mercury and organic carbon in house dust of residents of the Tri-city and the surrounding area (Baltic Sea coast)

The purpose of this study was to find out what factors determine the deposition levels of mercury and organic carbon in household dust in the Tri-city region (southern Baltic Sea coast). Analyses were performed on samples collected over the period of 2 years, from 2013 to 2015, always in the heating season. The deposition of organic carbon was between 4and 210 mg m^-2 month^-1, while mercury deposition ranged from 4 to 1336 ng m^-2 month^-1. Deposition of mercury in household dust during the heating season was three times lower and deposition of organic carbon one and a half times lower than outdoor deposition obtained in the Baltic Sea region by other researchers. In the non-heating period, deposition of mercury in household dust was similar to outdoor deposition while deposition of OC was one and a half times higher. Both of the analyzed dust components reached higher deposition in rural areas than in cities, and both mercury and organic carbon were found to have higher deposition in single-family houses than in buildings housing several families. The increased level of OC was conditioned by the vicinity of the building to a road or street with a high level of traffic, and dust collected on the ground floor had higher Hg depositions. The presence of plants and pets, as well as smoking more than ten cigarettes per day, resulted in higher depositions of both the compounds present in household dust within the Tri-city region.

Effect of agriculture and vegetation on carbonaceous aerosol concentrations (PM2.5 and PM10) in Puszcza Borecka National Nature Reserve (Poland)

Elemental carbon (EC) and organic carbon (OC) concentrations were measured in PM2.5 and PM10 samples collected at Diabla Gora (Puszcza Borecka National Nature Reserve, Poland) between 1 January and 31 December 2009, to investigate the seasonal and daily concentration variations and source regions. Strict sampling and measurement procedure, together with analysis of air mass backward trajectories and pollutant markers, indicated that the most important sources of carbon in the aerosols over Diabla Gora were vegetation, agricultural activity, and biomass burning. The highest contribution of secondary organic carbon (SOC) in aerosol mass (70 %) was detected during summer as a result of increased vegetation. In spring and autumn, raised concentrations of primary OC, calcium, and potassium and the presence of ammonium nitrate were observed in aerosols due to emission from surrounding fields and forests, as well as from fires in Lithuania. Anthropogenic influence on the increase in concentration of all carbon species was observed only in winter, when air masses drifted in from habitations situated within a radius of 50 km from the Diabla Gora station. Transport was of sporadic significance to the measured concentrations, and only in PM2.5 when wind speed was close to 1 m s^-1. In this case, the concentration of EC rose several fold. Such a tendency was particularly noticeable with the influx of air masses from nearby cities and the Polish-Russian border, which is located 29 km away from the station.

Association of weather and air pollution interactions on daily mortality in 12 Canadian cities

It has been well established that both meteorological attributes and air pollution concentrations affect human health outcomes. We examined all cause nonaccident mortality relationships for 28 years (1981-2008) in relation to air pollution and synoptic weather type (encompassing air mass) data in 12 Canadian cities. This study first determines the likelihood of summertime extreme air pollution events within weather types using spatial synoptic classification. Second, it examines the modifying effect of weather types on the relative risk of mortality (RR) due to daily concentrations of air pollution (nitrogen dioxide, ozone, sulfur dioxide, and particulate matter <2.5 μm). We assess both single- and two-pollutant interactions to determine dependent and independent pollutant effects using the relatively new time series technique of distributed lag nonlinear modeling (DLNM). Results display dry tropical (DT) and moist tropical plus (MT+) weathers to result in a fourfold and twofold increased likelihood, respectively, of an extreme pollution event (top 5 % of pollution concentrations throughout the 28 years) occurring. We also demonstrate statistically significant effects of single-pollutant exposure on mortality (p < 0.05) to be dependent on summer weather type, where stronger results occur in dry moderate (fair weather) and DT or MT+ weather types. The overall average single-effect RR increases due to pollutant exposure within DT and MT+ weather types are 14.9 and 11.9 %, respectively. Adjusted exposures (two-way pollutant effect estimates) generally results in decreased RR estimates, indicating that the pollutants are not independent. Adjusting for ozone significantly lowers 67 % of the single-pollutant RR estimates and reduces model variability, which demonstrates that ozone significantly controls a portion of the mortality signal from the model. Our findings demonstrate the mortality risks of air pollution exposure to differ by weather type, with increased accuracy obtained when accounting for interactive effects through adjustment for dependent pollutants using a DLNM.