Land use change impacts on air quality and climate

High-spatial resolution ground-level ozone in Yunnan, China: A spatiotemporal estimation based on comparative analyses of machine learning models

Monitoring ground-level ozone concentrations is a critical aspect of atmospheric environmental studies. Given the existing limitations of satellite data products, especially the lack of ground-level ozone characterization, and the discontinuity of ground observations, there is a pressing need for high-precision models to simulate ground-level ozone to assess surface ozone pollution. In this study, we have compared several widely utilized ensemble learning and deep learning methods for ground-level ozone simulation. Furthermore, we have thoroughly contrasted the temporal and spatial generalization performances of the ensemble learning and deep learning models. The 3-Dimensional Convolutional Neural Network (3-D CNN) model has emerged as the optimal choice for evaluating the daily maximum 8-h average ozone in Yunnan Province. The model has good performance: a spatial resolution of 0.05° × 0.05° and strong predictive power, as indicated by a Coefficient of Determination (R2) of 0.83 and a Root Mean Square Error (RMSE) of 12.54 μg/m³ in sample-based 5-fold cross-validation (CV). In the final stage of our study, we applied the 3-D CNN model to generate a comprehensive daily maximum 8-h average ozone dataset for Yunnan Province for the year 2021. This application has furnished us with a crucial high-resolution and highly accurate dataset for further in-depth studies on the issue of ozone pollution in Yunnan Province.

Land-Use Decisions Have Substantial Air Quality Health Effects

Understanding how best to use limited land without compromising food security, health, and beneficial ecosystem functions is a critical challenge of our time. Ecosystem service assessments increasingly inform land-use decisions but seldom include the effects of land use on air quality, the largest environmental health risk. Here, we estimate and value the air quality health effects of potential land-use policies and projected trends in the United States, alongside carbon sequestration and economic returns to land, until 2051. We show that air quality health effects are of first-order importance in land-use decisions, often larger in value than carbon sequestration and economic returns combined. When air quality is properly accounted for, policies that appeared beneficial are shown to be detrimental and vice versa. Land-use-driven air quality impacts are largely from agricultural emissions and biogenic forest emissions, although incentives for reduced deforestation remain beneficial overall. Without evaluating air quality, we are unable to determine whether land-use decisions make us better or worse off.

Impacts of changes in climate, land use, and emissions on global ozone air quality by mid-21st century following selected Shared Socioeconomic Pathways

Surface ozone (O3) is a major air pollutant and greenhouse gas with significant risks to human health, vegetation, and climate. Uncertainties around the impacts of various critical factors on O3 is crucial to understand. We used the Community Earth System Model to investigate the impacts of land use and land cover change (LULCC), climate, and emissions on global O3 air quality under selected Shared Socioeconomic Pathways (SSPs). Our findings show that increasing forest cover by 20 % under SSP1 in East China, Europe, and the eastern US leads to higher isoprene emissions leading 2-5 ppb increase in summer O3 levels. Climate-induced meteorological changes, like rising temperatures, further enhance BVOC emissions and increase O3 levels by 10-20 ppb in urban areas with high NOx levels. However, higher BVOC emissions can reduce O3 levels by 5-10 ppb in remote environments. Future NOx emissions control reduces O3 levels by 5-20 ppb in the US and Europe in all SSPs, but reductions in NOx and changes in oxidant titration increase O3 in southeast China in SSP5. Increased NOx emissions in southern Africa and India significantly elevate O3 levels up to 15 ppb under different SSPs. Climate change is equally important as emissions changes, sometimes countering the benefits of emissions control. The combined effects of emissions, climate, and land cover result in worse O3 air quality in northern India (+40 %) and East China (+20 %) under SSP3 due to anthropogenic NOx and climate-induced BVOC emissions. Over the northern hemisphere, surface O3 decreases due to reduced NOx emissions, although climate and land use changes can increase O3 levels regionally. By 2050, O3 levels in most Asian regions exceed the World Health Organization safety limit for over 150 days per year. Our study emphasizes the need to consider complex interactions for effective air pollution control and management in the future.

Contribution of land use and cover change (LUCC) to the global terrestrial carbon uptake

Terrestrial carbon uptake is critical to the removal of greenhouse gases and mitigation of global warming, which are closely related to land use and cover change (LUCC). However, understanding terrestrial carbon uptake and the LUCC contribution remains unclear because of complex interactions with other drivers (particularly climate change). By proposing an innovative approach of "trajectory analysis", this study aimed to isolate the LUCC contribution to terrestrial carbon uptake over different scales. Methodologically, global land was first divided into sub-regions of land transformations and stable land trajectories. Then, the carbon uptake change in the stable land trajectory was taken as a synthetic influence of climate change, which was used as a reference to isolate the carbon uptake alternation generated from the LUCC contribution in the land transformation trajectories. Finally, future LUCC and the terrestrial carbon uptake response were predicted under different development pathways. The results showed the global mean net ecosystem production (NEP) was 27.44 ± 36.51 g C m-2 yr-1 in the past two decades (2001-2019), generating 3.15 ± 0.88 Pg C yr-1 of the total terrestrial carbon uptake. Both the NEP and total carbon uptake showed significant increasing trends. Specifically, the mean NEP increased from 17.96 g C m-2 yr-1 in 2001 to 37.37 g C m-2 yr-1 in 2019, with the trend written as y = 1.20× + 15.20 (R2 = 0.62, p < 0.01). Meanwhile, the total carbon uptake increased from 2.35 Pg C yr-1 in 2001 to 4.13 Pg C yr-1 in 2019, which could be written as y = 0.12× + 1.93 (R2 = 0.56, p < 0.01). Climate change acted as the dominant factor for the trends at the global scale, which contributed 21.26 g C m-2 yr-1 and 1.59 Pg C yr-1 of the mean NEP and total carbon uptake changes in the stable land trajectories (94.30 million km2 that covered 63.29 % of the global land area), and the historical LUCC contributed -6.30 g C m-2 yr-1 (-40.85 %) and - 0.046 Pg C yr-1 (-57.50 %) of the mean NEP and the total carbon uptake change in the land transformation trajectories (6.64 million km2 that covered 4.46 % of the global land area), respectively. The maximum LUCC contribution (-61.85 g C m-2 yr-1) to the mean NEP occurred in the land transformations from evergreen needleleaf forests to woody savannas, while the maximum contribution (-0.034 Pg C y-1) to total carbon uptake was in the deforested regions from evergreen broadleaf forests to woody savannas. Eight SSP-RCP scenarios predictions demonstrated that future terrestrial carbon uptake would increase by an average of 0.015 Pg C yr-1 in 2100 due to global afforestation. SSP4-3.4 and SSP5-3.4 had the greatest potential for increasing carbon uptake, which is expected to reach a maximum increase (0.045 Pg C yr-1) in 2100. In contrast, the minimum terrestrial carbon uptake would occur in SSP5-8.5, which had the highest CO2 emissions. In conclusion, although relatively limited at the global scale, LUCC (particularly forest change) exerted an unneglectable role on terrestrial carbon uptake in land transformation regions. The results of this study will help to clarify terrestrial carbon uptake dynamics and provide a basis for carbon neutral and climatic adaptation.

Spatio-temporal evolution of land use and its eco-environmental effects in the Caohai National Nature Reserve of China

With the rapid development of social economy, the ecological environment problems caused by the change of wetland land use have been widely concerned. This paper takes the Caohai National Nature Reserve (CNNR) of China as the research object on the basis of referring to previous research results. Firstly, the remote sensing data was employed to examine the spatio-temporal evolution process of the CNNR from three aspects: land use structure change, land use dynamic degree and land use space change. Then the change of ecological environment quality was studied from the greenness, the wetness, the dryness and the heat. Based on the spatiotemporal changes of land use types and ecological environment quality in the CNNR from 2000 to 2020, a comprehensive index, the remote sensing ecological index (RSEI), was constructed to analyze the ecological environmental effects of land use changes. The results indicate that the land use changes in the CNNR went through two major periods: first, a period of rapid decline in cultivated land, and second, a period of sharp increase in constructed land. During the period of rapid decline in cultivated land, the ecological environment quality in the study area showed an upward trend. However, during the period of increased constructed land, the ecological environment quality gradually stabilized. This study provides a basis for the coordinated development of the ecological environment and social economy in the CNNR area.

Significant land cover change in China during 2001-2019: Implications for direct and indirect effects on surface ozone concentration

China has become one of the most prominent areas of global land cover change during the past few decades. These changes can directly influence meteorological parameters thus further regulating tropospheric ozone (O3) formation. Moreover, changes in biogenic emissions due to land cover variation can also have an indirect effect on O3 concentration. This study applied the Community Multiscale Air Quality model to comprehensively evaluate the impacts of significant land cover change on O3 levels in China during summertime between 2001 and 2019. The results showed that the daily maximum 8-h average O3 concentration (MDA8 O3) increased by 3.6-8.9 μg/m3, 2.8-8.0 μg/m3, 3.8-9.6 μg/m3, -1.5-6.2 μg/m3, and -0.6-2.5 μg/m3 in Beijing-Tianjin-Hebei region, Yangtze River Delta, Pearl River Delta, Sichuan Basin, and Fenwei Plain, respectively, in response to land cover variation. The research identified that the direct effect was the primary factor in raising O3 levels which mainly altered O3 concentration by changing vertical import and dry deposition velocity. Moreover, land cover variation tended to decrease biogenic nitric oxide emission and increase biogenic volatile organic compounds emission on the whole, and cause an obvious increase of MDA8 O3 by 1.8-4.9 μg/m3 in Pearl River Delta due to the indirect effect. This study offered valuable insights into the impacts of land cover change on O3 levels, highlighting the need for policymakers to consider land cover variation on air pollutants concentration for devising comprehensive multi-pollutant control strategies.

A comparison of experience sampled hay fever symptom severity across rural and urban areas of the UK

Hay fever affects people differently and can change over a lifetime, but data is lacking on how environmental factors may influence this. This study is the first to combine atmospheric sensor data with real-time, geo-positioned hay fever symptom reports to examine the relationship between symptom severity and air quality, weather and land use. We study 36145 symptom reports submitted over 5 years by over 700 UK residents using a mobile application. Scores were recorded for nose, eyes and breathing. Symptom reports are labelled as urban or rural using land-use data from the UK's Office for National Statistics. Reports are compared with AURN network pollution measurements and pollen and meteorological data taken from the UK Met Office. Our analysis suggests urban areas record significantly higher symptom severity for all years except 2017. Rural areas do not record significantly higher symptom severity in any year. Additionally, symptom severity correlates with more air quality markers in urban areas than rural areas, indicating that differences in allergy symptoms may be due to variations in the levels of pollutants, pollen counts and seasonality across land-use types. The results suggest that a relationship exists between urban surroundings and hay fever symptoms.

Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border)

A dust storm is a major environmental problem affecting many arid regions worldwide. The novel contribution of this study is combining indicators extracted from RS- and statistic-based predictive models to spatial mapping of land susceptibility to dust emissions in a very important dust source area in the borders of Iran and Iraq (Khuzestan province in Iran and Al-Basrah and Maysan provinces in Iraq). In this research, remote sensing (RS) techniques and machine learning techniques, including multivariate adaptive regression spline (MARS), random forest (RF), and logistic regression (LR), were used for dust source identification and susceptibility map preparation. To this end, 152 DSA for the period of 2005-2020 were identified in the study area. Of these DSA data, 70% was assigned to the Dust Source Susceptibility Mapping (DSSM) (training dataset) and 30% to model validation. Consequently, six factors (i.e., soil, lithology, slope, normalized vegetation differential index (NDVI), geomorphology, and land use units) were prepared as DSA's independent and effective variables. The results of all three models indicated that land use had the most impact on DSA. The validation results of these models using the test data showed sub-curves of 0.92, 0.86, and 0.76 for the RF, MARS, and LR models, respectively. Also, results showed that the RF model outperformed MARS (AUC = 0.89) and LR (AUC = 0.78) methods. In all three models, high and very high susceptibility classes generally covered a large percentage of the case study. The highest percentage of dust source points was also in this susceptibility category. Overall, the results of this study can be useful for planners and managers to control and reduce the risk of negative dust consequences.

Short-term exposure to wildfire-related PM2.5 increases mortality risks and burdens in Brazil

To assess mortality risks and burdens associated with short-term exposure to wildfire-related fine particulate matter with diameter ≤ 2.5 μm (PM_2.5), we collect daily mortality data from 2000 to 2016 for 510 immediate regions in Brazil, the most wildfire-prone area. We integrate data from multiple sources with a chemical transport model at the global scale to isolate daily concentrations of wildfire-related PM_2.5 at a 0.25 × 0.25 resolution. With a two-stage time-series approach, we estimate (i) an increase of 3.1% (95% confidence interval [CI]: 2.4, 3.9%) in all-cause mortality, 2.6% (95%CI: 1.5, 3.8%) in cardiovascular mortality, and 7.7% (95%CI: 5.9, 9.5) in respiratory mortality over 0-14 days with each 10 μg/m³ increase in daily wildfire-related PM_2.5; (ii) 0.65% of all-cause, 0.56% of cardiovascular, and 1.60% of respiratory mortality attributable to acute exposure to wildfire-related PM_2.5, corresponding to 121,351 all-cause deaths, 29,510 cardiovascular deaths, and 31,287 respiratory deaths during the study period. In this study, we find stronger associations in females and adults aged ≥ 60 years, and geographic difference in the mortality risks and burdens.

Water Yield Alteration in Thailand’s Pak Phanang Basin Due to Impacts of Climate and Land-Use Changes

Climate and land-use change are important factors in the hydrological process. Climatic and anthropic changes have played a crucial role in surface runoff changes. The objective of this research was to apply land-use change and future climate change to predict runoff change in the Pak Phanang River Basin. The Cellular Automata (CA)-Markov model was used to predict the land-use change, while the climate data from 2025 to 2085 under RPC2.6, RPC4.5, and RPC8.5 were generated using the MarkSim model. Additionally, the Soil and Water Assessment Tool (SWAT) combined land-use change and the generated meteorological data to predict the runoff change in the study area. The results showed that the annual runoff in the area would increase in the upcoming year, which would affect the production of field crops in the lowland area. Therefore, a good water drainage system is required for the coming years. Since the runoff would be about 50% reduced in the middle and late 21st century, an agroforestry system is also suggested for water capturing and reducing soil evaporation. Moreover, the runoff change’s overall impact was related to GHG emissions. This finding will be useful for the authorities to determine policies and plans for climate change adaptation in the Malay Peninsula.

History of Land Cover Change on Santa Cruz Island, Galapagos

Islands are particularly vulnerable to the effects of land cover change due to their limited size and remoteness. This study analyzes vegetation cover change in the agricultural area of Santa Cruz (Galapagos Archipelago) between 1961 and 2018. To reconstruct multitemporal land cover change from existing land cover products, a multisource data integration procedure was followed to reduce imprecision and inconsistencies that may result from the comparison of heterogeneous datasets. The conversion of native forests and grasslands into agricultural land was the principal land cover change in the non-protected area. In 1961, about 94% of the non-protected area was still covered by native vegetation, whereas this had decreased to only 7% in 2018. Most of the agricultural expansion took place in the 1960s and 1970s, and it created an anthropogenic landscape where 67% of the area is covered by agricultural land and 26% by invasive species. Early clearance of native vegetation took place in the more accessible—less rugged—areas with deeper-than-average and well-drained soils. The first wave of settlement consisted of large and isolated farmsteads, with 19% of the farms being larger than 100 ha and specializing in diary and meat production. Over the period of 1961–1987, the number of farms doubled from less than 100 to more than 200, while the average farm size decreased from 90 to 60 ha/farmstead. Due to labor constraints in the agricultural sector, these farms opted for less labor-intensive activities such as livestock farming. New farms (popping up in the 1990s and 2000s) are generally small in size, with <5 ha per farmstead, and settled in areas with less favorable biophysical conditions and lower accessibility to markets. From the 1990s onwards, the surge of alternative income opportunities in the tourism and travel-related sector reduced pressure on the natural resources in the non-protected area.

Combined Effect of High-Resolution Land Cover and Grid Resolution on Surface NO2 Concentrations

High-resolution air quality simulations are often performed using different nested domains and resolutions. In this study, the variability of nitrogen dioxide (NO2) concentrations estimated from two nested domains focused on Portugal (D2 and D3), with 5 and 1 km horizontal grid resolutions, respectively, was investigated by applying the WRF-Chem model for the year 2015. The main goal and innovative aspect of this study is the simulation of a whole year with high resolutions to analyse the spatial variability under the simulation grids in conjunction with detailed land cover (LC) data specifically processed for these high-resolution domains. The model evaluation was focused on Portuguese air quality monitoring stations taking into consideration the station typology. As main results, it should be noted that (i) D3 urban LC categories enhanced pollution hotspots; (ii) generally, modelled NO2 was underestimated, except for rural stations; (iii) differences between D2 and D3 estimates were small; (iv) higher resolution did not impact model performance; and (v) hourly D2 estimates presented an acceptable quality level for policy support. These modelled values are based on a detailed LC classification (100 m horizontal resolution) and coarse spatial resolution (approximately 10 km) emission inventory, the latter suitable for portraying background air pollution problems. Thus, if the goal is to characterise urban/local-scale pollution patterns, the use of high grid resolution could be advantageous, as long as the input data are properly represented.

Satellite-Based Estimation of the Influence of Land Use and Cover Change on the Surface Shortwave Radiation Budget in a Humid Basin

The surface shortwave radiation budget (Rsn) is one of the main drivers of Earth’s ecosystems and varies with atmospheric and surface conditions. Land use and cover change (LUCC) alters radiation through biogeophysical effects. However, due to the complex interactions between atmospheric and surface factors, it is very challenging to quantify the sole impacts of LUCC. Based on satellite data from the Global Land Surface Satellite (GLASS) Product and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, this study introduces an observation-based approach for detecting LUCC influences on the Rsn by examining a humid basin over the Dongting Lake Basin, China from 2001 to 2015. Our results showed that the Rsn of the study area presented a decreasing trend due to the combined effects of LUCC and climate change. Generally, LUCC contributed −0.45 W/m2 to Rsn at the basin scale, which accounted for 2.53% of the total Rsn change. Furthermore, the LUCC contributions reached −0.69 W/m2, 0.21 W/m2, and −0.41 W/m2 in regions with land transitions of forest→grass, grass→forest, and grass→farmland, which accounted for 5.38%, −4.68%, and 2.40% of the total Rsn change, respectively. Physically, LUCC affected surface radiation by altering the surface properties. Specifically, LUCC induced albedo changes of +0.0039 at the basin scale and +0.0061, −0.0020, and +0.0036 in regions with land transitions of forest→grass, grass→forest, and grass→farmland, respectively. Our findings revealed the impact and process of LUCC on the surface radiation budget, which could support the understanding of the physical mechanisms of LUCC’s impact on ecosystems.

Estimating the Daily NO2 Concentration with High Spatial Resolution in the Beijing–Tianjin–Hebei Region Using an Ensemble Learning Model

Nitrogen dioxide (NO2) is an important pollutant related to human activities, which has short-term and long-term effects on human health. An ensemble learning model was constructed and applied to estimate daily NO2 concentrations in the Beijing–Tianjin–Hebei region between 2010 and 2016. A variety of predictive variables included satellite-based troposphere NO2 vertical column concentration, meteorology, elevation, gross domestic product (GDP), population, land-use variables, and road network. The ensemble learning model achieved two things: a 0.01° × 0.01° grid resolution and the estimation of historical data for the years 2010–2013. The ensemble model showed good performance, whereby the R2 of tenfold cross-validation was 0.72 and the R2 of test validation was 0.71. Meteorological hysteretic effects were incorporated into the model, where the one-day lagged boundary layer height contributed the most. The annual NO2 estimation showed little change from 2010 to 2016. The seasonal NO2 estimation from highest to lowest occurred in winter, autumn, spring, and summer. In the annual maps and seasonal maps, the NO2 estimations in the northwest region were lower than those in the southeast region, and there was a heavily polluted band in the south of the Taihang Mountains. In coastal areas, the annual NO2 estimations were higher than the NO2 monitored values. The drawback of the model is underestimation at high values and overestimation at low values. This study indicates that the ensemble learning model has excellent performance in the simulation of NO2 with high spatial and temporal resolution. Furthermore, the research framework in this study can be a generally applied for drawing implications for other regions, especially for other cities in China.

Simulation of streamflow and instream loads of total suspended solids and nitrate in a large transboundary river basin using Source model and geospatial analysis

The management of LULC changes in transboundary river basins continues to challenge water resources managers due to the differences in development and conservation priorities of the countries sharing the basin. While various watershed models (WMs) exist to support decision making, basin-wide sustainable application of the instituted WM depends on the management priorities, resources, data availability, and knowledge gaps at national and sub-basin levels. Building on the results of our prior comparative analysis of WMs for a large transboundary river basin, we applied the 'Source' model to the Lower Mekong Basin (LMB). The constructed LMB-Source model was evaluated based on its streamflow and instream total suspended solids (TSS) and nitrate loads simulative performances. A combination of predictive performance metrics (PPMs) and sophisticated hydrologic signatures were used to calibrate model parameters and diagnose the model performance. Calibration results indicated strong similarity between the simulated and observed time series data and were further confirmed by the validation results. The successful model calibration generated parameters that represent hydrologic response characteristics (HRCs) and overland TSS and nitrate generation and removal dynamics (GRDs) previously not available for the LMB. The HRCs and GRDs can be regionalised with physical attributes of the LMB in future studies which can be used to support the management of ungauged sub-basins. This study confirms Source's capability as a decision support tool for the management of transboundary river basins, and provides basin-specific values of HRCs and GRDs that can be used for a better evaluation of the potential effects of LULC changes.

Impact of Deforestation on Land–Atmosphere Coupling Strength and Climate in Southeast Asia

Southeast Asia (SEA) is a deforestation hotspot. A thorough understanding of the accompanying biogeophysical consequences is crucial for sustainable future development of the region’s ecosystem functions and society. In this study, data from ERA-Interim driven simulations conducted with the state-of-the-art regional climate model COSMO-CLM (CCLM; version 4.8.17) at 14 km horizontal resolution are analyzed over SEA for the period from 1990 to 2004, and during El Niño–Southern Oscillation (ENSO) events for November to March. A simulation with large-scale deforested land cover is compared to a simulation with no land cover change. In order to attribute the differences due to deforestation to feedback mechanisms, the coupling strength concept is applied based on Pearson correlation coefficients. The correlations were calculated based on 10-day means between the latent heat flux and maximum temperature, the latent and sensible heat flux, and the latent heat flux and planetary boundary layer height. The results show that the coupling strength between land and atmosphere increased for all correlations due to deforestation. This implies a strong impact of the land on the atmosphere after deforestation. Differences in environmental conditions due to deforestation are most effective during La Niña years. The strength of La Nina events on the region is reduced as the impact of deforestation on the atmosphere with drier and warmer conditions superimpose this effect. The correlation strength also intensified and shifted towards stronger coupling during El Niño events for both Control and Grass simulations. However, El Niño years have the potential to become even warmer and drier than during usual conditions without deforestation. This could favor an increase in the formation of tropical cyclones. Whether deforestation will lead to a permanent transition to agricultural production increases in this region cannot be concluded. Rather, the impact of deforestation will be an additional threat besides global warming in the next decades due to the increase in the occurrence of multiple extreme events. This may change the type and severity of upcoming impacts and the vulnerability and sustainability of our society.

Electrospinning of Metal-Organic Frameworks for Energy and Environmental Applications

Herein, recent developments of metal-organic frameworks (MOFs) structured into nanofibers by electrospinning are summarized, including the fabrication, post-treatment via pyrolysis, properties, and use of the resulting MOF nanofiber architectures. The fabrication and post-treatment of the MOF nanofiber architectures are described systematically by two routes: i) the direct electrospinning of MOF-polymer nanofiber composites, and ii) the surface decoration of nanofiber structures with MOFs. The unique properties and performance of the different types of MOF nanofibers and their derivatives are explained in respect to their use in energy and environmental applications, including air filtration, water treatment, gas storage and separation, electrochemical energy conversion and storage, and heterogeneous catalysis. Finally, challenges with the fabrication of MOF nanofibers, limitations for their use, and trends for future developments are presented.

Phenanthroimidazole derivatives as a chemosensor for picric acid: a first realistic approach

A series of phenanthroimidazole (PI) derivatives (M1–M3): 2-phenyl-1H-phenanthro [9,10-d]imidazole (M1), 2-anthryl-1H-phenanthro[9,10-d]imidazole (M2), and 2-pyrenyl-1H-phenanthro[9,10-d]imidazole (M3) were synthesized and characterized using various spectroscopic techniques.

Assessing the Impact of Land-Use Planning on the Atmospheric Environment through Predicting the Spatial Variability of Airborne Pollutants

As an important contributor to pollutant emissions to the atmosphere, land use can degrade environmental quality. In order to assess the impact of land-use planning on the atmosphere, we propose a methodology combining the land-use-based emission inventories of airborne pollutants and the long-term air pollution multi-source dispersion (LAPMD) model in this study. Through a case study of the eastern Chinese city of Lianyungang, we conclude that (1) land-use-based emission inventorying is a more economical way to assess the overall pollutant emissions compared with the industry-based method, and the LAPMD model can map the spatial variability of airborne pollutant concentrations that directly reflects how the implementation of the land-use planning (LUP) scheme impacts on the atmosphere; (2) the environmental friendliness of the LUP scheme can be assessed by an overlay analysis based on the pollution concentration maps and land-use planning maps; (3) decreases in the emissions of SO₂ and PM₁₀ within Lianyungang indicate the overall positive impact of land-use planning implementation, while increases in these emissions from certain land-use types (i.e., urban residential and transportation lands) suggest the aggravation of airborne pollutants from these land parcels; and (4) the city center, where most urban population resides, and areas around key plots would be affected by high pollution concentrations. Our methodology is applicable to study areas for which meteorological data are accessible, and is, therefore, useful for decision making if land-use planning schemes specify the objects of airborne pollutant concentration.

Air Pollution and Climate Change Effects on Allergies in the Anthropocene: Abundance, Interaction, and Modification of Allergens and Adjuvants

Air pollution and climate change are potential drivers for the increasing burden of allergic diseases. The molecular mechanisms by which air pollutants and climate parameters may influence allergic diseases, however, are complex and elusive. This article provides an overview of physical, chemical and biological interactions between air pollution, climate change, allergens, adjuvants and the immune system, addressing how these interactions may promote the development of allergies. We reviewed and synthesized key findings from atmospheric, climate, and biomedical research. The current state of knowledge, open questions, and future research perspectives are outlined and discussed. The Anthropocene, as the present era of globally pervasive anthropogenic influence on planet Earth and, thus, on the human environment, is characterized by a strong increase of carbon dioxide, ozone, nitrogen oxides, and combustion- or traffic-related particulate matter in the atmosphere. These environmental factors can enhance the abundance and induce chemical modifications of allergens, increase oxidative stress in the human body, and skew the immune system toward allergic reactions. In particular, air pollutants can act as adjuvants and alter the immunogenicity of allergenic proteins, while climate change affects the atmospheric abundance and human exposure to bioaerosols and aeroallergens. To fully understand and effectively mitigate the adverse effects of air pollution and climate change on allergic diseases, several challenges remain to be resolved. Among these are the identification and quantification of immunochemical reaction pathways involving allergens and adjuvants under relevant environmental and physiological conditions.

Aerosols in the Pre-industrial Atmosphere

PURPOSE OF REVIEW

We assess the current understanding of the state and behaviour of aerosols under pre-industrial conditions and the importance for climate.

RECENT FINDINGS

Studies show that the magnitude of anthropogenic aerosol radiative forcing over the industrial period calculated by climate models is strongly affected by the abundance and properties of aerosols in the pre-industrial atmosphere. The low concentration of aerosol particles under relatively pristine conditions means that global mean cloud albedo may have been twice as sensitive to changes in natural aerosol emissions under pre-industrial conditions compared to present-day conditions. Consequently, the discovery of new aerosol formation processes and revisions to aerosol emissions have large effects on simulated historical aerosol radiative forcing.

SUMMARY

We review what is known about the microphysical, chemical, and radiative properties of aerosols in the pre-industrial atmosphere and the processes that control them. Aerosol properties were controlled by a combination of natural emissions, modification of the natural emissions by human activities such as land-use change, and anthropogenic emissions from biofuel combustion and early industrial processes. Although aerosol concentrations were lower in the pre-industrial atmosphere than today, model simulations show that relatively high aerosol concentrations could have been maintained over continental regions due to biogenically controlled new particle formation and wildfires. Despite the importance of pre-industrial aerosols for historical climate change, the relevant processes and emissions are given relatively little consideration in climate models, and there have been very few attempts to evaluate them. Consequently, we have very low confidence in the ability of models to simulate the aerosol conditions that form the baseline for historical climate simulations. Nevertheless, it is clear that the 1850s should be regarded as an early industrial reference period, and the aerosol forcing calculated from this period is smaller than the forcing since 1750. Improvements in historical reconstructions of natural and early anthropogenic emissions, exploitation of new Earth system models, and a deeper understanding and evaluation of the controlling processes are key aspects to reducing uncertainties in future.

The Essential Role for Laboratory Studies in Atmospheric Chemistry

Laboratory studies of atmospheric chemistry characterize the nature of atmospherically relevant processes down to the molecular level, providing fundamental information used to assess how human activities drive environmental phenomena such as climate change, urban air pollution, ecosystem health, indoor air quality, and stratospheric ozone depletion. Laboratory studies have a central role in addressing the incomplete fundamental knowledge of atmospheric chemistry. This article highlights the evolving science needs for this community and emphasizes how our knowledge is far from complete, hindering our ability to predict the future state of our atmosphere and to respond to emerging global environmental change issues. Laboratory studies provide rich opportunities to expand our understanding of the atmosphere via collaborative research with the modeling and field measurement communities, and with neighboring disciplines.

Introductory lecture: atmospheric chemistry in the Anthropocene

The term “Anthropocene” was coined by Professor Paul Crutzen in 2000 to describe an unprecedented era in which anthropogenic activities are impacting planet Earth on a global scale. Greatly increased emissions into the atmosphere, reflecting the advent of the Industrial Revolution, have caused significant changes in both the lower and upper atmosphere. Atmospheric reactions of the anthropogenic emissions and of those with biogenic compounds have significant impacts on human health, visibility, climate and weather. Two activities that have had particularly large impacts on the troposphere are fossil fuel combustion and agriculture, both associated with a burgeoning population. Emissions are also changing due to alterations in land use. This paper describes some of the tropospheric chemistry associated with the Anthropocene, with emphasis on areas having large uncertainties. These include heterogeneous chemistry such as those of oxides of nitrogen and the neonicotinoid pesticides, reactions at liquid interfaces, organic oxidations and particle formation, the role of sulfur compounds in the Anthropocene and biogenic–anthropogenic interactions. A clear and quantitative understanding of the connections between emissions, reactions, deposition and atmospheric composition is central to developing appropriate cost-effective strategies for minimizing the impacts of anthropogenic activities. The evolving nature of emissions in the Anthropocene places atmospheric chemistry at the fulcrum of determining human health and welfare in the future.

Co-benefits of global and regional greenhouse gas mitigation on U.S. air quality in 2050

Policies to mitigate greenhouse gas (GHG) emissions will not only slow climate change, but can also have ancillary benefits of improved air quality. Here we examine the co-benefits of both global and regional GHG mitigation on U.S. air quality in 2050 at fine resolution, using dynamical downscaling methods, building on a previous global co-benefits study (West et al., 2013). The co-benefits for U.S. air quality are quantified via two mechanisms: through reductions in co-emitted air pollutants from the same sources, and by slowing climate change and its influence on air quality, following West et al. (2013). Additionally, we separate the total co-benefits into contributions from domestic GHG mitigation versus mitigation in foreign countries. We use the WRF model to dynamically downscale future global climate to the regional scale, the SMOKE program to directly process global anthropogenic emissions into the regional domain, and we provide dynamical boundary conditions from global simulations to the regional CMAQ model. The total co-benefits of global GHG mitigation from the RCP4.5 scenario compared with its reference are estimated to be higher in the eastern U.S. (ranging from 0.6-1.0 μg m-3) than the west (0-0.4 μg m-3) for PM2.5, with an average of 0.47 μg m-3 over U.S.; for O3, the total co-benefits are more uniform at 2-5 ppb with U.S. average of 3.55 ppb. Comparing the two mechanisms of co-benefits, we find that reductions of co-emitted air pollutants have a much greater influence on both PM2.5 (96% of the total co-benefits) and O3 (89% of the total) than the second co-benefits mechanism via slowing climate change, consistent with West et al. (2013). GHG mitigation from foreign countries contributes more to the U.S. O3 reduction (76% of the total) than that from domestic GHG mitigation only (24%), highlighting the importance of global methane reductions and the intercontinental transport of air pollutants. For PM2.5, the benefits of domestic GHG control are greater (74% of total). Since foreign contributions to co-benefits can be substantial, with foreign O3 benefits much larger than those from domestic reductions, previous studies that focus on local or regional co-benefits may greatly underestimate the total co-benefits of global GHG reductions. We conclude that the U.S. can gain significantly greater domestic air quality co-benefits by engaging with other nations to control GHGs.