Observations of a "weekend effect" in diurnal temperature range

The effects of diurnal temperature range on mortality and emergency department presentations in Victoria state of Australia: A time-series analysis

State of Victoria, Australia (SVA) has a wide variation of diurnal temperatures (DTR). DTR has been reported to be associated with risk of mortality and morbidity. We examined the association between exposure to DTR and risk of all-cause mortality and emergency department (ED) presentations in the SVA. We obtained data on daily counts of deaths and ED presentations, and weather data from 1 st January 2000─2019. We applied a quasi-Poisson time-series regression analysis to examine the association between daily DTR exposures and risk of mortality and ED presentations. The analyses were queried by age, sex, seasons, ED presentations triages, and departure status. Risk of mortality and ED presentation increased by 0.33% (95% CI: 0.24%-0.43%), and 0.094% (95% CI: 0.077%-0.11%) in relation to one degree increase in the daily DTR. The association between DTR and ED presentations was stronger in children (0-15 years) (0.38% [95% CI: 0.34%-0.42%]) and the elderly (75+ years) (0.34% [95% CI: 0.29%-0.39%]). Resuscitation, which was consistently accounted for the highest vulnerability to DTR variation, increased by 0.79% (95% CI: 0.60%-0.99%). This study suggests that the risk of mortality and ED presentations associates with the increase of DTR. Children, the elderly, and their caregivers need to be made aware of the health risk posed by DTR.

Changing weekend effects of air pollutants in Beijing under 2020 COVID-19 lockdown controls

In 2020, lockdown control measures were implemented to prevent a novel coronavirus disease 19 (COVID-19) pandemic in many places of the world, which largely reduced human activities. Here, we detect changes in weekly cycles of PM2.5, NO2, SO2, CO and O3 concentrations in 2020 compared to 2018 and 2019 using the observed data at 32 stations in Beijing. Distinct weekly cycles of annual average PM2.5, NO2, SO2 and CO concentrations existed in 2018, while the weekend effects changed in 2020. In addition, the weekly cycle magnitudes of PM2.5, NO2, SO2, and O3 concentrations in 2020 decreased by 29.60-69.26% compared to 2018, and 4.49-47.21% compared to 2019. We propose that the changing weekend effects and diminishing weekly cycle magnitudes may be tied to the COVID-19 lockdown controls, which changed human working and lifestyle cycles and reduced anthropogenic emissions of air pollutants on weekends more than weekdays.

Changes in temperature and precipitation extremes in the arid regions of China during 1960–2016

Extreme climate events have a greater impact on natural and human systems than average climate. The spatial and temporal variation of 16 temperature and nine precipitation extremal indices was investigated using the daily maximum and minimum surface air temperature and precipitation records from 113 meteorological stations in China’s arid regions from 1960 to 2016. The warmth indices [warm spell duration (WSDI); numbers of warm nights, warm days, tropical nights (TR), and summer days (SU)] increased significantly. On the contrary, the cold indices [numbers of frost days (FD), ice days (ID), cool days, and cool nights; cold spell duration (CSDI)] decreased significantly. The number of FD decreased fastest (−3.61 days/decade), whereas the growing season length (GSL) increased fastest (3.17 days/decade). The trend was strongest for diurnal temperature range (DTR) (trend rate = −7.29, P < 0.001) and minimum night temperature (trend rate = 7.70, P < 0.001). The cold extreme temperature events increased with increasing latitude, but the warm extreme temperature events decreased. Compared with temperature indices, the precipitation indices exhibited much weaker changes and less spatial continuity. Overall, changes in precipitation extremes present wet trends, although most of the changes are insignificant. The regionally averaged total annual precipitation for wet days increased by 4.78 mm per decade, and extreme precipitation events have become more intense and frequent during the study period. The spatial variability of extreme precipitation in the region was primarily influenced by longitude. Furthermore, the climate experienced a warm-wet abrupt climate change during 1990s.

Seasonal prediction of daily PM2.5 concentrations with interpretable machine learning: a case study of Beijing, China

Machine learning (ML) has shown high predictive ability in environmental research. Accurate estimation of daily PM_2.5 concentrations is a prerequisite to address environmental public health issues. However, studies on the interpretability of ML algorithms were limited. In this study, we aimed to estimate the daily concentrations of PM_2.5 at a seasonal level, and to understand the potential mechanisms of ML algorithms' decisions with SHapley Additive exPlanations (SHAP). Daily ground PM_2.5 concentrations and meteorological data were obtained from the Beijing Municipal Ecological and Environmental Monitoring Center, and China Meteorological Data Service Centre between December 2013 and 2019 November. We calculated correlation coefficient and variance inflation factor (VIF) to eliminate the variables with collinearity, and recursive feature elimination (RFE) was further used to selected more important predictors. A series of ML algorithms, including linear regression, the variants of linear regression (Ridge, Lasso, Elasticnet), decision tree (DT), k-nearest neighbor (KNN), support vector regression (SVR), ensemble methods (random forest: RF, eXtreme Gradient Boosting: XGBoost), and deep learning (long short-term memory network: LSTM), were developed to estimate seasonal-level daily PM_2.5 concentrations. A 10-fold cross validation was used to tune hyperparameters, and root mean square error (RMSE), mean absolute error (MAE), ratio of performance to deviation (RPD), and Lin's concordance correlation coefficient (LCCC) were used to evaluate models' performance. SHAP was performed for local and global interpretability analysis. The results showed that the distribution of PM_2.5 concentrations in Beijing showed obvious seasonal patterns. A total of five variables (Precipitation, Mean wind speed, Sunshine duration, Mean surface temperature, Mean relative humidity) were selected for final prediction. LSTM showed much higher accuracy than other traditional ML models, achieved the smallest RMSE of 19.58 µg/m³ and MAE of 15.11 µg/m³. In terms of selected data set, there was acceptable (LCCC = 0.41 ~ 0.52) agreement and accuracy (RPD = 0.97 ~ 1.92) for LSTM. The SHAP analyses revealed that the meteorological factors had different influences in specific predictions, and the complex interactions were also illustrated. These results enhance our understanding of meteorological factors-PM_2.5 relationships and explain the mechanisms of ML algorithms' decisions.

Opportunistic experiments to constrain aerosol effective radiative forcing

Aerosol-cloud interactions (ACIs) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The nonlinearity of cloud-state changes to aerosol perturbations make it challenging to attribute causality in observed relationships of aerosol radiative forcing. Using correlations to infer causality can be challenging when meteorological variability also drives both aerosol and cloud changes independently. Natural and anthropogenic aerosol perturbations from well-defined sources provide "opportunistic experiments" (also known as natural experiments) to investigate ACI in cases where causality may be more confidently inferred. These perturbations cover a wide range of locations and spatiotemporal scales, including point sources such as volcanic eruptions or industrial sources, plumes from biomass burning or forest fires, and tracks from individual ships or shipping corridors. We review the different experimental conditions and conduct a synthesis of the available satellite datasets and field campaigns to place these opportunistic experiments on a common footing, facilitating new insights and a clearer understanding of key uncertainties in aerosol radiative forcing. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. Opportunistic experiments have significantly improved process-level understanding of ACI, but it remains unclear how reliably the relationships found can be scaled to the global level, thus demonstrating a need for deeper investigation in order to improve assessments of aerosol radiative forcing and climate change.

Competing PM2.5 and NO2 holiday effects in the Beijing area vary locally due to differences in residential coal burning and traffic patterns

The holiday effect is a useful tool to estimate the impact on air pollution due to changes in human activities. In this study, we assessed the variations in concentrations of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) during the holidays in the heating season from 2014 to 2018 based on daily surface air quality monitoring measurements in Beijing. A Generalized Additive Model (GAM) is used to analyze pollutant concentrations for 34 sites by comprehensively accounting for annual, monthly, and weekly cycles as well as the nonlinear impacts of meteorological factors. A Saturday effect was found in the downtown area, with about 4% decrease in PM_2.5 and 3% decrease in NO₂ relative to weekdays. On Sundays, the PM_2.5 concentrations increased by about 5% whereas there were no clear changes for NO₂. In contrast to the small effect of the weekend, there was a strong holiday effect throughout the region with average increases of about 22% in PM_2.5 and average reductions of about 11% in NO₂ concentrations. There was a clear geographical pattern in the strength of the holiday effect. In rural areas the increase in PM_2.5 is related to the proportion of coal and biomass consumption for household heating. In the suburban areas between the Fifth Ring Road and Sixth Ring Road there were larger reductions in NO₂ than downtown which might be due to decreased traffic as many people return to their hometown for the holidays. This study provides insights into the pattern of changes in air pollution due to human activities. By quantifying the changes, it also provides insights for improvements in air quality due to control policies implemented in Beijing during the heating season.

Modulations of surface thermal environment and agricultural activity on intraseasonal variations of summer diurnal temperature range in the Yangtze River Delta of China

Compared with interdecadal, interannual, or seasonal scales, the variations of diurnal temperature range (DTR) at the intraseasonal scale and their driving forces are less understood. Using surface meteorological observations and multi-source satellite retrievals during 2013-2017, together with Random Forest modeling, this study examines the intraseasonal variation of summer DTR in the Yangtze River Delta (YRD) region, China, and determines its potential driving factors [i.e., daily maximum/minimum surface air temperature (SAT_max/SAT_min), sunshine duration (SSD), rainfall, altitude, land vegetation cover, and land surface thermal environment including daytime/nighttime land surface temperature (LST_D/LST_N) and anthropogenic heat flux (AHF)]. It is found that the intraseasonal variation of DTR at both 8-day and monthly scales in the YRD exhibits regional differences and is modulated by different primary factors across the region. The evident intraseasonal variation of DTR, with a peak in June, in the northern YRD, is largely attributable to nighttime temperatures (SAT_min and LST_N), which in turn are mainly attributable to different LST_N responses to the underlying surface cover changes associated with crop rotation. In contrast, as the YRD metropolitan area (MYRD) is covered by a large proportion of built-up surfaces, and the weather stations there are surrounded by a higher surface thermal environment and AHF, the MYRD has stably higher LST and SAT_min in the whole summer season. Thus, the summer DTR in the MYRD exhibits marginal intraseasonal variations. In the southern YRD, there is also a distinct DTR characteristic, with a maximum in July and minimum in June, since this region is largely covered by forests with constantly high-density vegetation cover, and its DTR variation is mainly forced by SSD, which directly affects SAT_max. The findings reported here have important implications for understanding the influences of human activities on regional climate and environmental change for other regions of the world that experience various external forcings.

Inferring Changes in Summertime Surface Ozone-NOx-VOC Chemistry over U.S. Urban Areas from Two Decades of Satellite and Ground-Based Observations

Urban ozone (O₃) formation can be limited by NO_x, VOCs, or both, complicating the design of effective O₃ abatement plans. A satellite-retrieved ratio of formaldehyde to NO₂ (HCHO/NO₂), developed from theory and modeling, has previously been used to indicate O₃ formation chemistry. Here, we connect this space-based indicator to spatiotemporal variations in O₃ recorded by on-the-ground monitors over major U.S. cities. High-O₃ events vary nonlinearly with OMI HCHO and NO₂, and the transition from VOC-limited to NO_x-limited O₃ formation regimes occurs at higher HCHO/NO₂ value (3 to 4) than previously determined from models, with slight intercity variations. To extend satellite records back to 1996, we develop an approach to harmonize observations from GOME and SCIAMACHY that accounts for differences in spatial resolution and overpass time. Two-decade (1996-2016) multisatellite HCHO/NO₂ captures the timing and location of the transition from VOC-limited to NO_x-limited O₃ production regimes in major U.S. cities, which aligns with the observed long-term changes in urban-rural gradient of O₃ and the reversal of O₃ weekend effect. Our findings suggest promise for applying space-based HCHO/NO₂ to interpret local O₃ chemistry, particularly with the new-generation satellite instruments that offer finer spatial and temporal resolution.

Exploring the Holiday Effect on Air Temperatures

Anthropogenic emissions are generally lower during holidays than they are on workdays, this pattern is expected to result in temperature variations. Variations in the daily maximum (T_max), mean (T_mean) and minimum (T_min) air temperatures and the diurnal temperature range (DTR) during the Chinese New Year holiday are evaluated with two methods using daily meteorological observations collected at 2200 stations in China from 1961 to 2015. These two methods yield nearly equivalent results that reflect strong variations in the defined holiday effects. During the period from 1961 to 1980, T_mean, T_max, T_min and the DTR all exhibit cooling holiday effects, this effect as measured by the DTR disappears during the period from 1981 to 2000. However, during the period from 2001 to 2015 warming holiday effects are observed for T_max and the DTR. The evaluation shows that the holiday effect is neither unique nor statistically significant. These results indicate that the holiday effect is primarily caused by natural atmospheric oscillations, because ΔT oscillates noticeably with periods of approximately 7.1 days, 8.5 days and 16.2 days, and these oscillations can account for approximately 75.6% of the variance in ΔT. The oscillation identified here is consistent with the fundamental theory of Rossby wave in the atmosphere.

The Temporal and Spatial Distribution Characteristics of Air Pollution Index and Meteorological Elements in Beijing, Tianjin, and Shijiazhuang, China

With rapid economic development and continuous population growth, several important cities in China suffer serious air pollution, especially in the Beijing-Tianjin-Hebei economic developing area. Based on the daily air pollution index (API) and surface meteorological elements in Beijing, Tianjin, and Shijiazhuang (the capital of Hebei province) from 2001 to 2010, the relationships between API and meteorological elements were analyzed. The statistical analysis focused on the relationships at seasonal and monthly average scales, on different air pollution grades and air pollution processes. The results revealed that the air pollution conditions in the 3 areas gradually improved from 2001 to 2010, especially during summer; the worst conditions in air quality were recorded in Beijing in spring due to the influences of dust, and in Tianjin and Shijiazhuang in winter due to household heating. Meteorological elements exhibited different influences on air pollution, showing similar relationships between API in monthly averages and 4 meteorological elements (i.e., the average, maximum, and minimum temperatures; maximum air pressure; vapor pressure; and maximum wind speed), whereas the relationships on a seasonal average scale demonstrated significant differences. Compared with seasonal and monthly average scales of API, the relation coefficients based on different air pollution grades were significantly lower, whereas the relationship between API and meteorological elements based on air pollution processes reduced the smoothing effect due to the average processing of seasonal and monthly API and improved the accuracy of the results. Finally, statistical analysis of the distribution of pollution days in different wind directions indicated the directions of extreme and maximum wind speeds that mainly influence air pollution, representing valuable information that could support the definition of air pollution control strategies through the identification of the regions (and the located emission sources) where the implementation of emission reduction actions should be focused. Integr Environ Assess Manag 2018;14:710-721. © 2018 SETAC.

Southeast Atmosphere Studies: learning from model-observation syntheses

Concentrations of atmospheric trace species in the United States have changed dramatically over the past several decades in response to pollution control strategies, shifts in domestic energy policy and economics, and economic development (and resulting emission changes) elsewhere in the world. Reliable projections of the future atmosphere require models to not only accurately describe current atmospheric concentrations, but to do so by representing chemical, physical and biological processes with conceptual and quantitative fidelity. Only through incorporation of the processes controlling emissions and chemical mechanisms that represent the key transformations among reactive molecules can models reliably project the impacts of future policy, energy and climate scenarios. Efforts to properly identify and implement the fundamental and controlling mechanisms in atmospheric models benefit from intensive observation periods, during which collocated measurements of diverse, speciated chemicals in both the gas and condensed phases are obtained. The Southeast Atmosphere Studies (SAS, including SENEX, SOAS, NOMADSS and SEAC4RS) conducted during the summer of 2013 provided an unprecedented opportunity for the atmospheric modeling community to come together to evaluate, diagnose and improve the representation of fundamental climate and air quality processes in models of varying temporal and spatial scales. This paper is aimed at discussing progress in evaluating, diagnosing and improving air quality and climate modeling using comparisons to SAS observations as a guide to thinking about improvements to mechanisms and parameterizations in models. The effort focused primarily on model representation of fundamental atmospheric processes that are essential to the formation of ozone, secondary organic aerosol (SOA) and other trace species in the troposphere, with the ultimate goal of understanding the radiative impacts of these species in the southeast and elsewhere. Here we address questions surrounding four key themes: gas-phase chemistry, aerosol chemistry, regional climate and chemistry interactions, and natural and anthropogenic emissions. We expect this review to serve as a guidance for future modeling efforts.

Modeling respiratory illnesses with change point: A lesson from the SARS epidemic in Hong Kong

It is generally agreed that respiratory disease is closely related to ambient air quality and weather conditions. Besides, hygiene related factors such as the public health measures by the government and possible personal awareness in the community can also affect the spread of infectious respiratory diseases. However, there is no quantitative support for this conclusion, because of lack of quality data. The severe acute respiratory syndrome (or SARS) outbreak in 2003 triggered strict public health measures and personal awareness in the prevention of infectious respiratory diseases, providing us an opportunity to quantify the impact of hygiene related factors in the spread of the disease. In this paper, we model the number of the respiratory illnesses by a semiparametric model which models the environmental and weather impacts using a multiple index model and the impact of other public health measures and possible personal awareness using a growth curve with jump. Using data from Hong Kong, we found that public health measures contributed to about 39% of reduction in the number of respiratory illnesses during the SARS period. However, the impact of hygienically related factors eventually fades as time passes. The results provide indirect quantitative support to the usefulness of governmental campaigns to arouse the awareness of the public in staying away from transmission of respiratory diseases during the full outbreak of the disease. The results also show the fast fading of alertness of Hong Kong people towards the epidemic. Furthermore, our model also offers a way to model the impacts of environmental factors on respiratory diseases, when the data contains the effect of human intervention, by introducing the change point and growth curve to remove such an effect.

Cumulative effects of air pollution on public health

Cumulative effect is an important way through which the pollutants affect public health. However, few existing dynamical models are well enough understood and documented to detect or quantify the cumulative effects and to answer pertinent questions posed by the World Health Organization (WHO): 'Is there a threshold below which no effects of the pollutants on health are expected to occur in all people?' and 'What averaging period (time pattern) is the most relevant from the point of view of health?'. Using a new semi-parametric time series modelling approach, which incorporates non-linearity and latent cumulative variables, we show that the cumulative effects on health due to continual exposure to environmental pollutants can be very serious even at levels below the national ambient air quality standards of America (NAAQS). The situation is especially worrying for chronic sufferers. Our study suggests that different pollutants may require different cumulative periods (on average) to impact on health but they share a similar functional form in respect of their impact. We also suggest some possible revision of the ambient air quality standards.