Meteorological Influences Research Articles

Understanding and revealing the intrinsic impacts of the COVID-19 lockdown on air quality and public health in North China using machine learning.

To avoid the spread of COVID-19, China implemented strict prevention and control measures, resulting in dramatic variations in urban and regional air quality. With the complex effect from long-term emission mitigation and meteorology variation, an accurate evaluation of the net effect from lockdown on air quality changes has not been fully quantified. Here, we combined machine learning algorithm and Theil–Sen regression technique to eliminate meteorological and long-term trends effects on air pollutant concentrations and precisely detect concentrations changes those ascribed to lockdown measures in North China. Our results showed that, compared to the same period in 2015–2019, the adverse meteorology during the lockdown period (January 25th to March 15th) in early 2020 increased PM2.5 concentration in North China by 9.8 %, while the reduction of anthropogenic emissions led to a 32.2 % drop. Stagnant meteorological conditions have a more significant impact on the ground-level air quality in the Beijing-Tianjin-Hebei Region than that in Shanxi and Shandong provinces. After further striping out the effect of long-term emission reduction trend, the lockdown-derived NO2, PM2.5, and O3 shown variety change trend, and at −30.8 %, −27.6 %, and +10.0 %, respectively. Air pollutant changes during the lockdown could be overestimated up to 2-fold without accounting for the influences of meteorology and long-term trends. Further, with pollution reduction during the lockdown period, it would avoid 15,807 premature deaths in 40 cities. If with no deteriorate meteorological condition, the total avoided premature should increase by 1146.

The need for multifaceted approaches when dealing with the differing impacts of natural disasters and anthropocentric events on air quality

Air pollution associated health issues are increasing globally. This is due to both anthropogenic sources, such as traffic, and natural sources, such as bushfires. Natural disasters, such as bushfires, impact air quality by releasing large concentrations of pollutants affecting respiratory health. However, another recent global event has also had severe impacts on the environment and health, the global COVID-19 pandemic. Global pandemics, such as COVID-19, can also influence air quality by altering human activity, resulting in its own associated health impacts. This study aimed to investigate the impact of a natural disaster and global pandemic on outdoor ambient air pollution by quantifying and comparing the spatial distribution of two air pollutants, nitrogen dioxide (NO2) and particulate matter (PM10), during the different periods across the Greater Sydney region, Australia, while correcting for anthropogenic sources and meteorological influences such as temperature and rain. COVID-19 and bushfire affected periods were compared to a control period when both of these influences were absent. We found that NO2 was significantly higher during the COVID-19 pandemic than during the control period and the recent 2019 bushfires. Conversely, PM10 was significantly lower during the COVID-19 pandemic than the bushfire and control periods. The spatial distribution of both pollutants and influencers also varied across the study site. These results suggest that both events markedly impacted air quality, although they impacted the air pollutants differently. These findings further demonstrate a greater need to understand the impact of natural disasters and anthropocentric events on air pollution as multifaceted, spatially relevant policies are required to address these events, particularly if they increase in frequency or severity in the future.

The Spatial-Temporal Differentiation of Aerosol Optical Properties and Types in the Beijing–Tianjin–Hebei Region Based on the Ecological Functional Zones

Atmospheric aerosol pollution has seriously affected the ecological environment and human health in recent years. There are great differences in aerosol optical properties and types due to the influence of environmental conditions, meteorology, industrial and agricultural activities, and other factors of each ecological functional zones. Using MODIS aerosol products (including MCD19A2 and MOD04_3K), this study discussed the temporal and spatial distribution of aerosol optical depth (AOD), Ångström wavelength index (AE) and aerosol types in the Beijing–Tianjin–Hebei region (BTH region) based on the ecological functional zones from 2015 to 2020. The results showed as follows: (1) The AOD in BTH region showed an obviously spatial pattern of low in the north and high in the south, while the spatial pattern of AE was opposite to that of AOD. In addition, the dominant aerosol type of the north part was clean aerosol, the dominant aerosol type of the middle part was biomass burning or urban-industrial aerosol, while that of the other part was mixed aerosols. (2) The seasonal changes of AOD and AE indexes in each ecological functional area had obvious seasonal changes, and the AOD and AE values were highest in summer. At the same time, the proportion of biomass combustion or urban industrial aerosol was the highest in summer. (3) The ecological functional areas with fewer human activities were dominated by clean aerosols, with lower AOD and higher AE value. The ecological functional areas dominated by cities were dominated by mixed aerosols, with higher AOD. The ecological functional areas dominated by agriculture and heavy industry were dominated by biomass combustion or urban industrial aerosols, with the largest AOD. (4) Compared with 2015, the average AOD of each ecological functional area decreased significantly to 2020, and biomass combustion or urban industrial aerosols changed to mixed aerosols.

Simulating the Impact of the Sustained Melting Arctic on the Global Container Sea–Rail Intermodal Shipping

Global warming trends and the rapid reduction of summer Arctic sea ice extent have increased the feasibility of transarctic transport. How the process of glacier melting affects the existing containerized sea–rail shipping network and container flow assignment has become a challenging economic and policy issue. This paper first examines the meteorological influences on glacier melting and the assignment of container flow over the existing sea–rail network. Then, a three-layer simulation framework is constructed, with the upper layer simulating glacier melting based on the raster grid, the middle layer combining a grid and topology analysis to simulate the evolution of the global sea–rail network and the lower layer establishing a concave cost network flow model to simulate the container flow assignment. Finally, we use MicroCity to achieve the dynamic optimization and simulation of global container flow assignment, solving the large-scale sea–rail shipping network traffic assignment problem. The simulation results show that the proposed model and solution algorithm are feasible and effective, revealing the variation of container flow assignment in the global sea–rail shipping network under different Arctic ice melting scenarios. For instance, in the summer of 2050, the Arctic routes will share the global container flows, resulting in a significant reduction of container flows in the Malacca Strait, Suez Canal and Panama Canal.

Research Trends, Hotspots and Frontiers of Ozone Pollution from 1996 to 2021: A Review Based on a Bibliometric Visualization Analysis

A total of 2932 publications in the field of ozone pollution were obtained from the Web of Science and briefly reviewed using a bibliometric analysis and WOS-based citation reports. CiteSpace 5.7.R3 (64 bit) was used to perform a visualization of knowledge mapping by keywords co-words, burst analysis, co-cited reference analysis, and cooperation network analysis. Research topics in this field have mainly focused on three aspects: risk assessment of ozone pollution for both humans and plants under short-term and long-term exposure; ozone pollution characterization and modeling of ozone transport on different scales; and elucidating the mechanism of ozone formation and source apportionment. By clustering the co-cited references using the data from 2016 to 2021, the frontiers are found to be: (1) VOCs’ precursors and ozone transformation mechanism; (2) modeling of source apportionment and source-oriented chemical transport considering meteorological influence to predict ozone concentration at different spatial and temporal scales; and (3) premature mortality and health burden with relation to ozone exposure. It should be mentioned that an emerging research hotspot is the utilization of artificial intelligence (AI) tools (e.g., machine learning, deep learning, etc.) to facilitate the modeling of big data at different scales.

Size distribution, meteorological influence and uncertainty for source-specific risks: PM2.5 and PM10-bound PAHs and heavy metals in a Chinese megacity during 2011–2021

This study aims at exploring size distribution, meteorological influence and uncertainty for source-specific risks of atmospheric particulate matter (PM), which can improve risk-mitigation strategies for health protection. Heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) in PM2.5 and PM10 were detected in a Chinese megacity during 2011–2021. A new method named as PMFBMR, which combines the Positive Matrix Factorization, Bootstrapping, Mote Carlo and Risk assessment model, was developed to estimate uncertainty of source-specific risks. It was found that PAH risks concentrated in fine PM, while HMs showed high risks in both fine and coarse PMs. For PM2.5, HQ (non-cancer risk hazard quotient) of gasoline combustion (GC), diesel and heavy oil combustion (DC), coal combustion (CC), industrial source (IS), resuspended dust (RD) and secondary and transport PM (ST) were 0.6, 1.4, 0.9, 1.6, 0.3, and 0.3. ILCR (lifetime cancer risk) of sources were IS (9.2E-05) > DC (2.6E-05) = CC (2.6E-05) > RD (2.2E-05) > GC (1.7E-05) > ST (6.4E-06). PM2.5 from GC, DC, CC and IS caused higher risks than coarse PM, while coarse PM from RD caused higher risks. Source-specific risks were influenced not only by emissions, but also by meteorological condition and dominant toxic components. Risks of GC and DC were usually high during stable weather. Some high risks of CC, IS and RD occurred at strong WS due to transport or wind-blown resuspension. GC and DC risks (influenced by both PAHs and HMs) showed strong relationship with T, while IS and RD risks (dominated by HMs) showed weak link with meteorological conditions. For uncertainty of source-specific risks, HQ and ILCR were sensitive for different variables, because they were dominated by components with different uncertainties. When using source-specific risks for risk-mitigation strategies, the focused toxic components, used toxic values, PM sizes and uncertainty are necessary to be considered.

Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions.

Evaluating the influence of anthropogenic-emission changes on air quality requires accounting for the influence of meteorological variability. Statistical methods such as multiple linear regression (MLR) models with basic meteorological variables are often used to remove meteorological variability and estimate trends in measured pollutant concentrations attributable to emission changes. However, the ability of these widely used statistical approaches to correct for meteorological variability remains unknown, limiting their usefulness in the real-world policy evaluations. Here, we quantify the performance of MLR and other quantitative methods using simulations from a chemical transport model, GEOS-Chem, as a synthetic dataset. Focusing on the impacts of anthropogenic-emission changes in the US (2011 to 2017) and China (2013 to 2017) on PM2.5 and O3, we show that widely used regression methods do not perform well in correcting for meteorological variability and identifying long-term trends in ambient pollution related to changes in emissions. The estimation errors, characterized as the differences between meteorology-corrected trends and emission-driven trends under constant meteorology scenarios, can be reduced by 30%-42% using a random forest model that incorporates both local- and regional-scale meteorological features. We further design a correction method based on GEOS-Chem simulations with constant-emission input and quantify the degree to which anthropogenic emissions and meteorological influences are inseparable, due to their process-based interactions. We conclude by providing recommendations for evaluating the impacts of anthropogenic-emission changes on air quality using statistical approaches.

Assessment of the effect of meteorological and emission variations on winter PM2.5 over the North China Plain in the three-year action plan against air pollution in 2018–2020

Air quality in Beijing-Tianjin-Hebei and its surrounding regions has been significantly improved during the Three-Year Action Plan for Winning the Battle Against Air Pollution from 2018 to 2020. The PM2.5 concentration can be significantly influenced by meteorological conditions, and the effectiveness of anthropogenic emissions reduction on PM2.5 over the North China Plain remains unclear. In this study, the Nested Air Quality Model System (NAQPMS) and observations were used to quantitatively examine the contribution of meteorological and emission variations to autumn-winter (November to February) PM2.5 concentrations in Beijing and its surrounding regions from 2018 to 2020. The model reproduced the temporal evolution of atmospheric pollutants and components well. The results showed that the observed mean PM2.5 concentrations during the winter were reduced by 15% and 29% in 2019 and 2020, respectively, compared with that in 2018. In 2019, the meteorological conditions over the Beijing-Tianjin-Hebei (BTH) region were poor, causing an increase of PM2.5 by 7.6% in BTH and 11% in Beijing; therefore, the control of air pollutant emission compensated for the unfavorable influence of meteorology and emission reduction was the decisive factor for PM2.5 reduction; In 2020, the meteorological conditions were relatively favorable, and the changes in meteorological factors and emission reduction led to a decrease in PM2.5 concentration by 18.6% and 10.5%, respectively, compared to 2018. Emission reduction also contributed to decrease in nitrate, ammonium, organic matter, element carbon and precursors. This work confirmed the obvious environmental benefit of pollution control measures from 2018 to 2020. There was obvious regional heterogeneity in the effect of meteorology and the impact of adverse meteorological conditions should be considered during the formulation of air pollution control measures. The results can serve as a reference for the formulation of air quality objectives or the evaluation of the environmental effect of pollution control schemes.

Contributions of meteorology to ozone variations: Application of deep learning and the Kolmogorov-Zurbenko filter

From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutional neural network and Shapely additive explanation (SHAP) to examine the complex underlying nonlinearity between variations of surface ozone and meteorological factors. Results of the models showed that between 2000 and 2019, specific humidity (38% and 27%) and temperature (28% and 37%) contributed the most to ozone formation over the Houston and Dallas metropolitan areas, respectively. On the other hand, the results show that solar radiation (50%) strongly impacted ozone variation over West Texas during this time. Using a combination of the Kolmogorov-Zurbenko (KZ) filter and multiple linear regression, we also evaluated the influence of meteorology on ozone and quantified the contributions of meteorological parameters to trends in surface ozone formation. Our findings showed that in Houston and Dallas, meteorology influenced ozone variations to a large extent. The impacts of meteorology on West Texas, however, showed meteorological factors had fewer influences on ozone variabilities from 2000 to 2019. This study showed that SHAP analysis and the KZ approach can investigate the contributions of the meteorological factors on ozone concentrations and help policymakers enact effective ozone mitigation policies.

One-year Simulation of Air Pollution in Central China, Characteristics, Distribution, Inner Region Cross-transmission, and Pathway Research in 18 Cities

Air pollution in Henan province is serious and is significantly impacted by pollution transmission and interactions with surrounding areas. The emission sources in 18 cities in Henan province were labeled and applied to the WRF-CMAQ traceability model for simulation in January, April, July, and October of 2017. The pollutant distribution results showed that due to the combined influence of emissions and meteorology, the concentrations of PM2.5, NO2, and SO2 in Henan province were the highest in winter and the lowest in summer. The seasonal variation in O3-8h concentration was the highest in summer, followed by spring, and the lowest in winter. There was a large difference in pollutant concentrations between different seasons. The average concentrations of PM2.5, NO2, and SO2 in winter in Henan province were 4.17, 4.12, and 6.24 times those in summer, respectively, whereas the concentration of O3-8h in summer was 2.24 times that in winter. Since PM2.5, NO2, and SO2 are closely related to primary emissions and have a certain homology, the distributions of high values of these three pollutants were higher in the north and lower in the south, and the seasonal trends were more consistent. The seasonal distribution of O3-8h varied widely, with high O3-8h values mainly distributed in the northeastern region of Henan province in summer, when meteorological conditions contributed to O3 production; in winter, spring, and autumn, high O3-8h values were mainly distributed in the southern part of Henan province due to the suppression of meteorological conditions and NOx consumption. The results of the study on the transport of pollutants showed that extra-provincial transport and natural sources contributed the most to the concentrations of PM2.5, O3-8h, NO2, and SO2 in winter, with 36.20%-72.32%, 77.96%-96.08%, 49.45%-78.80%, and 59.05%-88.85%, respectively. When considering only local emissions and intra-provincial transmission, the contributions of emissions to local concentrations of the four pollutants in summer were the highest in all cities of Henan province. The contributions of intra-provincial transmission to PM2.5 and O3-8h in spring were the largest, with 25.63%-74.69% and 30.21%-80.01%, respectively, and the contributions of intra-provincial transmission to NO2 and SO2 in winter were larger, with 26.02%-76.96% and 20.30%-82.34%. The transmission paths of PM2.5, NO2, and SO2 were more similar in Henan province, with more transmission from north to south in winter, from west to east and southwest to northeast in spring, from southwest to northeast in summer, and from north to south in autumn; however, the transmission of PM2.5 was more complicated. The O3-8h transport path was more different from the others, especially in autumn when pollutants were mostly transported from north to south, but the O3-8h transport path from southwest to northeast was obvious.

Characterization of winter air pollutant gradients near a major highway

Traffic-related air pollutants (TRAP) including nitric oxide (NO), nitrogen oxide (NOx), carbon monoxide (CO), ultrafine particles (UFP), black carbon (BC), and fine particulate matter (PM2.5) were simultaneously measured at near-road sites located at 10 m (NR10) and 150 m (NR150) from the same side of a busy highway to provide insights into the influence of winter time meteorology on exposure to TRAP near major roads. The spatial variabilities of TRAP were examined for ambient temperatures ranging from −11 °C to +19 °C under downwind, upwind, and stagnant air conditions. The downwind TRAP concentrations at NR10 were higher than the upwind concentrations by a factor of 1.4 for CO to 13 for NO. Despite steep downwind reductions of 38 % to 75 % within 150 m, the downwind concentrations at NR150 were still well above upwind concentrations. Near-road concentrations of NOx and UFP increased as ambient temperatures decreased due to elevated emissions of NOx and UFP from vehicles under colder temperatures. Traffic-related PM2.5 sources were identified using hourly PM2.5 chemical components including organic/inorganic aerosol and trace metals at both sites. The downwind concentrations of primary PM2.5 species related to tailpipe and non-tailpipe emissions at NR10 were substantially higher than the upwind concentrations by a factor of 4 and 32, respectively. Traffic-related PM2.5 sources accounted for almost half of total PM2.5 mass under downwind conditions, leading to a rapid change of PM2.5 chemical composition. Under stagnant air conditions, the concentrations of most TRAP and related PM2.5 including tailpipe emissions, secondary nitrate, and organic aerosol were comparable to, or even greater than, the downwind concentrations under windy conditions, especially at NR150. This study demonstrates that stagnant air conditions further widen the traffic-influenced area and people living near major roadways may experience increased risks from elevated exposure to traffic emissions during cold and stagnant winter conditions.

Evidence of stockpile contamination for legacy polychlorinated biphenyls and organochlorine pesticides in the urban environment of Cyprus (Eastern Mediterranean): Influence of meteorology on air level variability and gas/particle partitioning based on equilibrium and steady-state models

The present study investigated comprehensively the atmospheric occurrence and fate of an extensive range of polychlorinated biphenyls (PCBs; forty-two congeners), organochlorine pesticides (OCPs; twenty-seven emerging and legacy agrochemicals) and polycyclic aromatic hydrocarbons (PAHs; fifty parent and alkylated members, including the non USEPA-16 listed toxic ones), in both gas and particulate phase of the scarcely monitored atmosphere over Cyprus for the first time. Parent-metabolite concentration ratios suggested fresh application for dichlorodiphenyl-trichloroethanes (DDTs), dicofol, hexachlorocyclohexanes, endosulfan and chlorothalonil, particularly during spring (April-May). Regressions of logarithms of partial pressure against ambient temperature revealed that secondary recycling from contaminated terrestrial surfaces regulates the atmospheric level variability of PCBs, DDTs, aldrin, chlordane, dicofol, heptachlor and endosulfan. Enthalpies of surface-air exchange (∆HSA) calculated from Clausius-Clapeyron equations were significantly correlated to vaporization enthalpies (∆HV) determined by chromatographic techniques, corroborating presence of potential stockpile-contaminated sites around the study area. The Harner-Bidleman equilibrium model simulating urban areas, and the Li-Jia empirical model, predicted better the partitioning behavior of PAHs (<four-ring parent and alkylated members), PCBs (<hexa-chlorobiphenyls), and OCPs, respectively. For heavier PAHs and PCBs, partitioning coefficients (KP) were inadequately predicted by the Li-Ma-Yang steady-state model, probably due to local human activities and regional transport in the study area.

Atmospheric odor dispersion from oil refinery flare system: a case study.

A flare stack is an indispensable device in the oil and gas industry for allowing the safe combustion of gases into the atmosphere, especially during emergencies. However, it is not ideal for the routine disposal of gaseous waste, as it is subject to meteorological influences and poor operational control. In addition, it can be affected by toxic currents and thus pose a potential risk of odors; in view of this, an assessment must be made of the implications of burning on the environment and health. Atmospheric dispersion modelling has proved to be a very useful tool for this purpose. In light of this, an attempt has been made in this work to evaluate the impact of H2S on the well-being (odor perception) of the community in the surrounding area of an oil refinery, where the temporary burning of rich gas in H2S occurs in a chemical flare, and operational factors have an influence on atmospheric dispersion. The odor assessment was carried out with the aid of AERMOD which was adapted to estimate the maximum odor concentration for very short periods by means of peak-to-mean ratios. The results showed that H2S detection can reach a probability rate of 42% at 3.5km distance from the flare (in a time interval of 5s) with a relatively high degree of annoyance (3.0 AU). However, some operational procedures can reduce the probability of odor detection to 29% and the degree of annoyance to 2.6 AU.

Meteorological Influence of Mineral Dust Distribution Over South-Western Africa Deserts Using Reanalysis and Satellite Data

This study investigated the meteorological influence of mineral dust in the south-western African region using satellite and reanalysis datasets by studying 1) the seasonal transport and distribution of mineral dust in the region; 2) the relationship between precipitation, wind and desert dust, and 3) the long-term trends of dust column density, precipitation rate and surface wind speed. The results show that the Namib desert is the main source of dust in the region, with the density and distribution varying by seasons. The study found that the greatest dust distribution occurs in the June-July-August (JJA) season, attributed to the southwesterly winds which transport the dust into the interior of the region. Moreover, small dust aerosols less that 20 µm are observed at highest altitudes between 7 and 10.28 km. Favourable meteorological such as strong and fast winds and low precipitation, and low vegetation played a vital role in the production and distribution of dust aerosols. Over a long time (i.e., from 1990 to 2020), dust column density trend increased, while precipitation and surface wind speed trends decreased. This study provides significant basis for assessing and monitoring of the desertification processes and their effect on regional climate variability and change in Southwest Africa, where data is ground-based data is scarce and related efforts are rare.

Variation of pollution sources and health effects on air pollution before and during COVID-19 pandemic in Linfen, Fenwei Plain

Stringent pollution control measures are generally applied to improve air quality, especially in the Spring Festival in China. Meanwhile, human activities are reduced significantly due to nationwide lockdown measures to curtail the COVID-19 spreading in 2020. Herein, to better understand the influence of control measures and meteorology on air pollution, this study compared the variation of pollution source and their health risk during the 2019 and 2020 Spring Festival in Linfen, China. Results revealed that the average concentration of PM2.5 in 2020 decreased by 39.0% when compared to the 2019 Spring Festival. Organic carbon (OC) and SO42- were the primary contributor to PM2.5 with the value of 19.5% (21.1%) and 23.5% (25.5%) in 2019 (2020) Spring Festival, respectively. Based on the positive matrix factorization (PMF) model, six pollution sources of PM2.5 were indicated. Vehicle emissions (VE) had the maximum reduction in pollution source concentration (28.39 μg· m-3), followed by dust fall (DF) (11.47 μg· m-3), firework burning (FB) (10.39 μg· m-3), coal combustion (CC) (8.54 μg· m-3), and secondary inorganic aerosol (SIA) (3.95 μg· m-3). However, the apportionment concentration of biomass burning (BB) increased by 78.7%, indicating a significant increase in biomass combustion under control measures. PAHs-lifetime lung cancer risk (ILCR) of VE, CC, FB, BB, and DF, decreased by 44.6%, 43.2%, 34.1%, 21.3%, and 2.0%, respectively. Additionally, the average contribution of meteorological conditions on PM2.5 in 2020 increased by 20.21% compared to 2019 Spring Festival, demonstrating that meteorological conditions played a crucial role in located air pollution. This study revealed that the existing control measures in Linfen were efficient to reduce air pollution and health risk, whereas more BB emissions were worthy of further attention. Furthermore, the result was conducive to developing more effective control measures and putting more attention into unfavorable meteorological conditions in Linfen.

Long-Term Variations of Meteorological and Precursor Influences on Ground Ozone Concentrations in Jinan, North China Plain, from 2010 to 2020

Ground-level ozone (O3) pollution in the North China Plain has become a serious environmental problem over the last few decades. The influence of anthropogenic emissions and meteorological conditions on ozone trends have become the focus of widespread research. We studied the long-term ozone trends at urban and suburban sites in a typical city in North China and quantified the contributions of anthropogenic and meteorological factors. The results show that urban O3 increased and suburban O3 decreased from 2010 to 2020. The annual 90th percentile of the maximum daily 8-h average of ozone in urban areas increased by 3.01 μgm−3year−1 and, in suburban areas, it decreased by 3.74 μgm−3year−1. In contrast to the meteorological contributions, anthropogenic impacts are the decisive reason for the different ozone trends in urban and suburban areas. The rapid decline in nitrogen oxides (NOX) in urban and suburban areas has had various effects. In urban areas, this leads to a weaker titration of NOX and enhanced O3 formation, while in suburban areas, this weakens the photochemical production of O3. Sensitivity analysis shows that the O3 formation regime is in a transition state in both the urban and suburban areas. However, this tends to be limited to volatile organic compounds (VOCs) in urban areas and to NOX in suburban areas. One reasonable approach to controlling ozone pollution should be to reduce nitrogen oxide emissions while strengthening the control of VOCs.

Diesel passenger vehicle shares influenced COVID-19 changes in urban nitrogen dioxide pollution

Diesel-powered vehicles emit several times more nitrogen oxides than comparable gasoline-powered vehicles, leading to ambient nitrogen dioxide (NO2) pollution and adverse health impacts. The COVID-19 pandemic and ensuing changes in emissions provide a natural experiment to test whether NO2 reductions have been starker in regions of Europe with larger diesel passenger vehicle shares. Here we use a semi-empirical approach that combines in-situ NO2 observations from urban areas and an atmospheric composition model within a machine learning algorithm to estimate business-as-usual NO2 during the first wave of the COVID-19 pandemic in 2020. These estimates account for the moderating influences of meteorology, chemistry, and traffic. Comparing the observed NO2 concentrations against business-as-usual estimates indicates that diesel passenger vehicle shares played a major role in the magnitude of NO2 reductions. European cities with the five largest shares of diesel passenger vehicles experienced NO2 reductions times larger than cities with the five smallest diesel shares. Extending our methods to a cohort of non-European cities reveals that NO2 reductions in these cities were generally smaller than reductions in European cities, which was expected given their small diesel shares. We identify potential factors such as the deterioration of engine controls associated with older diesel vehicles to explain spread in the relationship between cities’ shares of diesel vehicles and changes in NO2 during the pandemic. Our results provide a glimpse of potential NO2 reductions that could accompany future deliberate efforts to phase out or remove passenger vehicles from cities.

Impact of the ‘Coal-to-Natural Gas’ Policy on Criteria Air Pollutants in Northern China

During the last decades, China had issued a series of stringent control measures, resulting in a large decline in air pollutant concentrations. To quantify the net change in air pollutant concentrations driven by emissions, we developed an approach of determining the closed interval of the deweathered percentage change (DPC) in the concentration of air pollutants on an annual scale, as well as the closed intervals of cumulative DPC in a year compared with that in the base year. Thus, the hourly mean mass concentrations of criteria air pollutants to determine their interannual variations and the closed intervals of their DPCs during the heating seasons from 2013 to 2019 in Qingdao (a coastal megacity) were analyzed. The seasonal mean SO2 concentration decreased from 2013 to 2019. The seasonal mean CO, NO2, and PM2.5 concentrations also generally decreased from 2013 to 2017, but increased unexpectedly in 2018 (from 0.9 mg m−3 (CO), 42 µg m−3 (NO2), and 51 µg m−3 (PM2.5) in 2017 to 1.1 mg m−3, 48 µg m−3, and 64 µg m−3 in 2018, respectively). The closed intervals of DPC in concentrations of CO, NO2, and PM2.5 from the 2017 heating season (2017/2018) to the 2018 heating season (2018/2019) were obtained at (27%, 30%), (15%, 18%), and (30%, 33%), respectively. Such high positive endpoint values of the closed intervals, in contrast to their small interval lengths, indicate increased emissions of these pollutants and/or their precursors in 2018/2019 compared with 2017/2018, by minimizing the meteorological influences. The rebounds of CO, NO2, and PM2.5 in 2018/2019 were likely associated with a doubled increase in natural gas (NG) consumption implemented by the “coal-to-NG” project, as the total energy consumption showed little difference. Our results suggested an important role of the “coal-to-NG” project in driving concentrations of air pollutant increases in China in 2018/2019, which need integrated assessments.

Calibration of Co-Located Identical PAR Sensors Using Wireless Sensor Networks and Characterization of the In Situ fPAR Variability in a Tropical Dry Forest

The fraction of photosynthetic active radiation (fPAR) attempts to quantify the amount of enery that is absorbed by vegetation for use in photosynthesis. Despite the importance of fPAR, there has been little research into how fPAR may change with biome and latitude, or the extent and number of ground networks required to validate satellite products. This study provides the first attempt to quantify the variability and uncertainties related to in-situ 2-flux fPAR estimation within a tropical dry forest (TDF) via co-located sensors. Using the wireless sensor network (WSN) at the Santa Rosa National Park Environmental Monitoring Super Site (Guanacaste, Costa Rica), this study analyzes the 2-flux fPAR response to seasonal, environmental, and meteorological influences over a period of five years (2013–2017). Using statistical tests on the distribution of fPAR measurements throughout the days and seasons based on the sky condition, solar zenith angle, and wind-speed, we determine which conditions reduce variability, and their relative impact on in-situ fPAR estimation. Additionally, using a generalized linear mixed effects model, we determine the relative impact of the factors above, as well as soil moisture on the prediction of fPAR. Our findings suggest that broadleaf deciduous forests, diffuse light conditions, and low wind patterns reduce variability in fPAR, whereas higher winds and direct sunlight increase variability between co-located sensors. The co-located sensors used in this study were found to agree within uncertanties; however, this uncertainty is dominated by the sensor drift term, requiring routine recalibration of the sensor to remain within a defined criteria. We found that for the Apogee SQ-110 sensor using the manufacturer calibration, recalibration around every 4 years is needed to ensure that it remains within the 10% global climate observation system (GCOS) requirement. We finally also find that soil moisture is a significant predictor of the distribution and magnitude of fPAR, and particularly impacts the onset of senescence for TDFs.

Impact of meteorology on indoor air quality, energy use, and health in a typical mid-rise multi-family home in the eastern United States.

Heating and cooling requirement differences across climates not only have carbon emissions and energy efficiency implications but also impact indoor air quality (IAQ) and health. Energy and IAQ building simulation models help understand tradeoffs or co-benefits, but these have not been applied to evaluate climate zone or multi-family home differences. We modeled a four-story multi-family home in six U.S. climate zones and quantified energy, IAQ, and health outcomes with EnergyPlus, CONTAM, and a pediatric asthma systems science model. Pollutant sources included cooking and ambient. Outputs were daily PM2.5 and NO2 indoor concentrations, infiltration, energy for heating and cooling, and asthma exacerbations, which were compared across climate zones, apartment units, and resident behaviors. Daily ambient-sourced PM2.5 decreased and cooking-sourced PM2.5 increased with higher ambient temperatures. Infiltration air changes per hour were higher on the first versus the fourth floor and in colder climates. Window opening during cooking led to decreases in total pollutant concentrations (11%-18% for PM2.5 and 9%-15% for NO2 ), 3%-4% decreases in asthma exacerbations within climate zones, and minimal impacts on cooling, but led to increased heating demand (4%-8%). Our results demonstrate the influence of meteorology, multi-family building characteristics, and resident behavior on IAQ, energy, and health, focused on multi-zone methodology.