Concentrations In Urban Areas Research Articles

Tropospheric NO2 Monitoring Using the Multi-Axis Differential Optical Absorption Spectroscopy in Urban Area

Spectroscopic methods have played an important role in the study of chemical and physical processes related to the composition of the atmosphere and the differential optical absorption spectroscopy (DOAS) has been one of the most powerful methods to measure a wide range of trace gases. The pollutants are identified by their respective ranges of wavelengths that must be previously known. A Passive Differential Optical Absorption Spectroscopy system that uses the ultraviolet region from 400 to 480 nm of the solar radiation is presented. In this research diurnal variation of NO2 was remotely measured by means of MAX-DOAS system which uses multiple viewing angles to monitor pollutant concentrations in urban area at the city of Rio de Janeiro. The instrument was placed on the roof of a building oriented to the center of the city. Tropospheric NO2 amounts are retrieved from the measured spectra using the DOAS technique. In this paper we give a general description of the procedure and present the results from measurements during four days in winter 2017 associated with the prevailing meteorological aspects. These days were characterized by mostly sunny and dry conditions, no convection, periods of medium clouds and clear sky. The tropospheric NO2 slant column densities values are presented and the results are consistent for all three used elevation angles (5º, 10º, and 15º). The results demonstrate the capability and the potential of the MAX-DOAS technique to derive information on spatial distribution of NO2 in an urban environment.

Biodiversity Conservation and Social Science Underpinnings in the Cordillera Highlands of Northern Philippines

This article conducts a literature review and synthesis of social science research on biodiversity in the Cordillera of Northern Philippines. The area hosts key biodiversity sites that need protection and conservation but there is little understanding of the socio-cultural, economic and political dynamics that cause the loss of biodiversity and effective conservation. The review showed unequal distribution of social-science among the different sites in the Cordillera with concentration in urban areas. The reviewed materials showed a wide scope of topics, indicating that the social dimension of biodiversity conservation is complex and multidimensional. For social science research to better inform decision-making in biodiversity conservation, social scientists need to be aggressive and openly engaged with practitioners and local policy makers so that relevant research findings could penetrate biodiversity management practice. Partnership among universities and productive research groups could address the disproportionate distribution of research efforts across sites.

Joint prediction of CO, NOx, NMHC pollutant concentrations in urban area

Primary air pollutants could directly and indirectly (through generating secondary air pollutants) threaten natural or human systems. In particular, urban air pollution issue becomes more and more significant in the recent decades, as a result of rapid urbanization. However, the estimation of multiple-pollutant concentrations is limited by high spatial and temporal variations, which hinders the accuracy of mechanistic modeling of air pollution. In this study, we employed an Artificial Neural Network (ANN) model to jointly predict multiple primary pollutants, including CO, NOx, and nan-methane hydrocarbons (NMHC), over the urban area of Italy. The results showed that performances of the ANN model (MSE and Pearson correlation) in joint prediction cross multiple pollutants were much better than in prediction of any single pollutant individually, indicating the joint measurements of multiple pollutants could favor the machine-learning model by providing useful information from one pollutant to predict another.

Impact of Urban Growth on Air Quality in Indian Cities Using Hierarchical Bayesian Approach

Several studies have found rising ambient particulate matter (PM 2.5 ) concentrations in urban areas across developing countries. For setting mitigation policies source-contribution is needed, which is calculated mostly through computationally intensive chemical transport models or manpower intensive source apportionment studies. Data based approach that use remote sensing datasets can help reduce this challenge, specially in developing countries which lack spatially and temporally dense air quality monitoring networks. Our objective was identifying relative contribution of urban emission sources to monthly PM 2.5 ambient concentrations and assessing whether urban expansion can explain rise of PM 2.5 ambient concentration from 2001 to 2015 in 15 Indian cities. We adapted the Intergovernmental Panel on Climate Change’s (IPCC) emission framework in a land use regression (LUR) model to estimate concentrations by statistically modeling the impact of urban growth on aerosol concentrations with the help of remote sensing datasets. Contribution to concentration from six key sources (residential, industrial, commercial, crop fires, brick kiln and vehicles) was estimated by inverse distance weighting of their emissions in the land-use regression model. A hierarchical Bayesian approach was used to account for the random effects due to the heterogeneous emitting sources in the 15 cities. Long-term ambient PM 2.5 concentration from 2001 to 2015, was represented by a indicator R (varying from 0 to 100), decomposed from MODIS (Moderate Resolution Imaging Spectroradiometer) derived AOD (aerosol optical depth) and angstrom exponent datasets. The model was trained on annual-level spatial land-use distribution and technological advancement data and the monthly-level emission activity of 2001 and 2011 over each location to predict monthly R. The results suggest that above the central portion of a city, concentration due to primary PM 2.5 emission is contributed mostly by residential areas (35.0 ± 11.9%), brick kilns (11.7 ± 5.2%) and industries (4.2 ± 2.8%). The model performed moderately for most cities (median correlation for out of time validation was 0.52), especially when assumed changes in seasonal emissions for each source reflected actual seasonal changes in emissions. The results suggest the need for policies focusing on emissions from residential regions and brick kilns. The relative order of the contributions estimated by this study is consistent with other recent studies and a contribution of up to 42.8 ± 14.1% is attributed to the formation of secondary aerosol, long-range transport and unaccounted sources in surrounding regions. The strength of this approach is to be able to estimate the contribution of urban growth to primary aerosols statistically with a relatively low computation cost compared to the more accurate but computationally expensive chemical transport based models. This remote sensing based approach is especially useful in locations without emission inventory.

Development of Ozone Prediction Model in Urban Area

One of the main challenges for countries in tropical area such as Malaysia is the high concentration of ozone (O3) caused by elevated levels of anthropogenic and natural ozone precursors. In this study, variation of O3 concentrations in urban area (Klang) was investigated using data covering three-year period (2012–2015) on hourly basis. Result shows that the diurnal cycle of ozone concentration has a mid-day peak (1400hrs) while lower concentration occurs at night time (2100hrs) as it titrates nitrogen dioxide (NO2). There exists statistically significant difference (p<0.05) of O3 concentration at study areas. Moderate Spearman correlation coefficient was evaluated between O3 and NO2 (r=0.45, p<0.05). Multiple linear regression (MLR) model was developed and signifies that nitrogen oxides (NO), relative humidity (RH), NO2, carbon monoxide (CO), wind speed (WS), temperature (T) and sulphur dioxide (SO2) are the significant predictors for O3 concentration. This study suggests that the emission of O3 precursors, particularly NOx from motor vehicles, needs to be controlled to reduce the incidence of high O3 levels in Malaysia.

Factors controlling dissolved 137Cs concentrations in east Japanese Rivers

To investigate the main factors that control the dissolved radiocesium concentration in river water in the area affected by the Fukushima Daiichi nuclear power plant accident, the correlations between the dissolved 137Cs concentrations at 66 sites normalized to the average 137Cs inventories for the watersheds with the land use, soil components, topography, and water quality factors were assessed. We found that the topographic wetness index is significantly and positively correlated with the normalized dissolved 137Cs concentration. Similar positive correlations have been found for European rivers because wetland areas with boggy organic soils that weakly retain 137Cs are mainly found on plains. However, for small Japanese river watersheds, the building area ratio in the watershed strongly affected the dissolved 137Cs concentration. One reason for this would be because the high concentrations of solutes, such as K+ and dissolved organic carbon, discharged in urban areas would inhibit 137Cs absorption to soil particles. A multiple regression equation was constructed to predict the normalized dissolved 137Cs concentration with the topography, land use, soil component, and water quality data as explanatory variables. The best model had the building land use as the primary predictor. When comparing two multiple regression models in which the explanatory variables were limited to (1) the land use and soil composition and (2) the water quality, the water quality model underestimated the high normalized dissolve 137Cs concentration in urban areas. This poor reproducibility indicates that the dissolved 137Cs concentration value in urban areas cannot be solely explained by the solid-liquid distribution of 137Cs owing to the influence of the water quality, but some specific 137Cs sources in urban areas would control the dissolved 137Cs concentration.

Contribution of Smart Cities to the Energy Sustainability of the Binomial between City and Country

Cities are at the center of the transition to a decarbonized economy. The high consumption of electricity in these urban areas causes them to be the main focus of greenhouse gas emissions. However, they have a high margin of capacity to increase energy efficiency and local energy generation. Along these lines, the smart urban management model has been proposed as a solution to the unsustainability of cities. Due to the global trend of population concentration in urban areas, cities tend to be representative of the population, energy consumption, and energy sustainability of their countries. Based on this hypothesis, this paper studied the relationship between the smart city model and the concept of energy sustainability. First, the research analyzed the relationship between urban population growth and energy sustainability; and then the self-consumption capacity of photovoltaic electricity of the main cities of the countries classified in the energy sustainability indicator (Energy Trilemma Index 2017) of the World Energy Council was analyzed. According to the results, the scope of action for self-consumption of photovoltaic electricity is broad and cities have the capacity to contribute significantly to the energy sustainability of their countries. Following the approach of other authors, the development of energy sustainability objectives and the installation of smart systems in distribution grids must be aligned with national objectives.

VCSEL-Based Atmospheric Trace Gas Sensor Using First Harmonic Detection

This paper presents a measurement system based on the first harmonic in tunable diode laser absorption spectroscopy using a vertical-cavity surface-emitting laser to measure the atmospheric CO2 and H2O concentrations. The developed system separates the residual amplitude modulation signal from the harmonics and then eliminates it. A digital signal processing is developed to autonomously infer the wavelength and light intensities of the laser. The gas concentrations are determined without extra calibration. The long-term measurements are taken to validate the precision and accuracy of the system. Based on the Allan variance analysis, the broad wavelength scanning enhances the measurement precision, and the first-harmonic detection can achieve about two times as high precision as the traditional second-harmonic detection. The field measurements implemented in early spring of 2018 in Munich were compared with the commercial nondispersive infrared (NDIR) sensor. The outcomes have revealed that our system has high accuracy for the gas concentration measurements and high consistency with the NDIR sensor. The diurnal variations of CO2 concentration have demonstrated that CO2 concentration in urban areas is affected by the biosphere and meteorological conditions and the daily anthropogenic activities. Furthermore, the air trajectory analysis based on the HYSPLIT model has found that the CO2 emission sources primarily come from the southeast of Munich. The developed system described in this paper has a great potential for in situ trace gas concentration measurements, the analysis on the polluted gas distributions, and the verification of the pollutant transport model in urban areas.

Estimating ground-level PM2.5 over a coastal region of China using satellite AOD and a combined model

Studies of adverse human health effects due to exposures to particles with an aerodynamic diameter smaller than 2.5 μm (PM2.5) are often limited by sparse ground in situ measurements. Satellite remote sensing technique provides an effective tool for PM2.5 assessment in areas where surface PM2.5 monitoring network is not available. The MODIS aerosol optical depth (AOD) with spatial resolution of 10 km has been widely used in retrieving PM2.5 concentrations in a large scale, but it is insufficient for city-scale PM2.5 studies. In this study, we used the newly released AOD product with higher resolution of 3 km incorporating meteorological fields from Goddard Earth Observing System-Forward Processing (GEOS-FP) and road density for ground-level PM2.5 estimation in Fuzhou, i.e. a coastal city of China. A two-stage statistical model combing linear mixed effects model (LME) and support vector regression model (SVR) was proposed in this study, and a 10-fold cross validation approach was employed for model validation. We obtained an overall R2 of 0.81, root mean square error (RMSE) of 8.83 μg/m3 in model fitting, and R2 of 0.77, RMSE of 9.51 μg/m3 in model validation. The retrieved PM2.5 presented a spatial pattern with high concentrations in urban areas and low values in suburban or mountainous areas. We also found that the spatial distribution of PM2.5 concentrations showed a very strong correlation with the terrain features, forest cover, road density, and industrial pollution sources. The results revealed that the combined LME-SVR model using 3 km AOD along with GEOS-FP field and road density could achieve high accuracy in PM2.5 estimation, and would be helpful for air quality monitoring in Fuzhou.

Measurement of size-fractionated particulate-bound mercury in Beijing and implications on sources and dry deposition of mercury

Particulate-bound mercury (PBM) plays an important role in the Hg cycling and is with high concentration in urban area. Totally 10-staged (range of 0.01–10μm) size-fractionated PBM was measured in Beijing during December 2016–November 2017. Our study indicated that Beijing suffered from heavy PBM pollution, with an annual average of 297.9 (17.2–1906.6) pgm−3, 22.2–52.5 times higher than that observed in North America. Overall, the concentration of PBM2.5 (PBM2.5 and PBM2.5–10 meant PBM concentration in PM with aerodynamic diameter <2.5μm and in the range of 2.5–10μm, respectively) accounted for 75%–87% of PBM10 in all seasons. The increase of PBM2.5–10 in the polluted days (PM2.5>75μgm−3) indicated the primary influence from natural sources while anthropogenic sources and secondary conversion caused the growth of PBM2.5 concentration. In addition, the generation of secondary PBM during the transport process contributed to the generation of PBM in Beijing, especially when the PM concentrations increase simultaneously. The local emissions and physicochemical conversion were the dominant cause of extremely high PBM pollution event in winter. Enhanced PBM concentrations also lead to severe dry deposition of PBM in Beijing. The estimated annual dry deposition of PBM was 40.6μgm−2yr−1, which was 3.1–203.0 times than observations from other sites in northern hemisphere. PBM2.5 contributed 53% of total dry deposition in all seasons, significantly higher than the other observation sites (28%–36%). In addition, the proportion of PBM2.5 deposition in PBM10 deposition ranged from 47% in summer to 66% in winter, which indicated that both seasonal variations of PBM size distribution characteristics should be taken into consideration. Results from this study revealed the size distribution and dry deposition characteristics of PBM in urban Beijing and provided base for the implementation of Minamata convention on mercury.

How does upgrading an emissions inventory affect air quality simulations?

Each year, all Member States (MS) have to deliver their national emissions inventory to the European Union for all activity sectors, following the requirements of the CLRTAP programme. Recently, the specifications of this emissions report changed, MS emissions data had to be reported in grid cells with a resolution of 0.5° × 0.5°, and now, from 2015 forward, they must use a higher resolution grid (0.1° × 0.1°). The purpose of this study is to investigate the main differences found between these two emissions inventories for Europe, focusing on Portugal as a case study, using their available common year (2015). Differences on emissions values and their spatial distribution were analysed per sector and pollutant. Additionally, to evaluate and compare the accuracy of both datasets, air quality modelling simulations were performed, and the resulting pollutant concentrations were validated using data from observations. The results found indicated major differences in several MS (e.g. France, Italy, Germany and Spain). Portugal was not one of the delta hotspots but significant differences were still found, mainly for NOx emissions for the transport sectors, both emissions and concentrations in urban areas, as well as NO2 concentrations throughout the study domain. The analysis of the air quality modelling outputs indicates that the EMEP0.1 inventory does not improve model performance, which suggests that the methodology to build EMEP0.1 was not adequate. This work highlights the importance of accurately estimating emissions data and confirms what other studies already indicated regarding uncertainties: solely improving the emissions inventory resolution does not necessarily imply higher accuracy in the results.

Fine-resolution mapping of particulate matter concentration in urban areas and population exposure analysis via dispersion modeling: a study in Daejeon, South Korea.

To deliver accurate particulate matter information to citizens, a detailed particulate matter dispersion model including factors such as land use and meteorological information was developed and used to create particulate matter concentration distribution maps for Daejeon Metropolitan City (South Korea). The results showed differences from existing particulate matter concentration distribution maps created using established methods. For PM2.5, approximately 3600 concentration maps were constructed. Taking a map as an example, according to existing methods, the PM2.5 concentration was "good" in 56% and "moderate" in 44% of areas. However, according to our modeling, the PM2.5 concentration was good in 31%, moderate in 26%, "unhealthy" in 28%, and "very unhealthy" in 15% of areas. Furthermore, the existing methods indicated that no portion of the population was exposed to poor particulate matter concentrations, while the proposed model found that over 170,000 people were exposed to such concentrations. These results will contribute to sustainable urban and environmental planning.

Impact of next-generation vehicles on tropospheric ozone estimated by chemical transport model in the Kanto region of Japan

The plans to introduce next-generation hybrid and zero-emission vehicles in the market are now enacted by governments in many countries to manage both global warming and air pollution problems. There are only a few studies evaluating the effects of the next-generation vehicles on the changes in concentrations of ozone generated by the photochemical reactions between volatile organic compounds and nitrogen oxides (NOx). To evaluate these changes, we performed chemical transport modeling in the Kanto region, Japan in the summer of 2013. The results show that if the vehicles are substituted by hybrid vehicles, average ozone concentrations increase in urban areas and decrease in suburban areas due to NOx titration. Substitution with zero-emission passenger vehicles decreases the concentrations in both urban and suburban areas. Substitution with both hybrid and zero-emission passenger and heavy-duty vehicles highly increases the concentrations in urban areas. Using the model results, we also discuss the effect of ozone concentration changes on premature mortality of humans in summer. The results suggest that, in some cases the introduction of next-generation vehicles might exasperate ozone concentrations, even leading to 5 to 10 times higher premature mortality during the summer compared to that of influenza and heat stroke in Japan.

Exploring the association between the built environment and remotely sensed PM2.5 concentrations in urban areas

Haze, especially PM2.5, poses a serious threat to public health in China. PM2.5 primarily originates from urban activities, and built environment may affect its formation and dispersion. Previous studies were based on limited data from ground-monitoring stations, and high resolution pollution maps are unavailable for statistical analyses. In this study, a 1 km*1 km wall-to-wall map of PM2.5 concentration is developed with remote sensing data in Wuhan, China, and spatial statistics are used to figure out the influence of the built environment on PM2.5 concentrations. In terms of land cover, high-rise high-density building areas have the largest impact on PM2.5 concentrations, and the effect of forestland on the concentrations is not obvious in winter. In terms of land use, industrial lands are unrelated to air pollution in the downtown, while transportation has become a main source of PM2.5 pollution. In terms of urban form, floor area ratio and building density are positively associated with PM2.5 concentrations, and different types of road densities have different effects on air pollution. Finally, the implications of the study for urban planning and development are given. It is necessary to develop a polycentric urban structure to balance high population density and reduce traffic emissions in downtown areas. Road and bus networks should be optimized simultaneously to reduce traffic emissions and “small blocks and narrow roads” may be considered as an alternative for urban development. The spatial morphology of streets and buildings should be considered during urban design and urban renewal. In general, the study contributes to the application of remote sensing in urban planning and development, and remotely sensed PM2.5 concentration data could provide further findings than the air pollution data obtained from ground monitoring and “bottom-up” models in past studies.

Correlations of PM10 concentrations in urban areas with vehicle fleet development, rain precipitation and diesel fuel sales

Although in many developed countries the green transition towards sustainable mobility is going faster and faster towards low or zero emission vehicles, road traffic is so far one of the key PM10 pollution source in urban areas. This research compares and analyses correlations between PM10 pollution trends in urban areas and the following parameters: vehicles fleet, rain precipitations and diesel fuel sales. The city of Rome has been selected as pilot area, analysing data gathered by its PM10 monitoring network between 2006 and 2017. Statistical correlations have been computed between all trends giving particular regard to the replicability of methods and strategies to other cities. The obtained results showed an integrated analysis of correlations between PM10 concentrations and vehicle fleet development, rain precipitation, diesel fuel sales, highlighting in which months the rain has a higher effect on PM10 concentrations; the differences between rain precipitation amounts and the number of rainy days per month; the possibility to forecast data using the statistical correlations analysis; the effects of vehicle fleet and diesel fuel sales on the PM10 concentrations.

Evidence of the mitigated urban particulate matter island (UPI) effect in China during 2000–2015

Urban fine particulate matter (PM2.5) pollution has been the subject of great concern, due to its remarkable adverse effects on public health. However, quantitative investigations of the spatial concentration trends from urban to background areas are still lacking. The urban particulate matter island (UPI) effect, referring to the phenomenon that higher particle concentrations in urban areas are gradually attenuated to background areas, is found and investigated in this study. UPI intensity (UPII) and its footprint (UPIFP) are defined to quantify the magnitude and extent of UPI, respectively. Based on observations from 338 Chinese prefectures for 2000–2015, we confirm the existence of the UPI effect, and further reveal its spatiotemporal patterns. We find that: 1) 84% (283/338) of the cities in China in various city levels and climatic zones showed the UPI phenomenon during 2000–2015, and this phenomenon is closely related to the land-use/cover patterns between the urban area and surrounding areas; 2) different spatial patterns of UPI effect are apparent, with high UPII values and small UPIFP values in Beijing-Tianjin-Hebei, moderate UPII values and large UPIFP values in northern China, the Pearl River Delta and the Yangtze River Delta, and high UPII and UPIFP values in the Western Taiwan Straits region; 3) UPI mitigation can be observed nationwide, with significant decreasing trends for both UPII and UPIFP, benefiting from the increase in urban green spaces and the built-up proportion differences between urban and suburban areas during urbanization. Additionally, it is indicated that more urban residents and faster urban expansion correspond to a steeper decline of UPII in China during 2000–2015. The existence and characteristics of the UPI effect in China will allow new insight and understanding of urban pollution patterns, and will provide scientific evidence for urban planning and pollution control.

Mitigation impact of roadside trees on fine particle pollution

Fine particulate matter (PM2.5) is an important air pollutant due to its adverse health effects. Vehicle emissions make a large contribution to particle concentrations in urban areas. Exposure to particles especially near roadways increases the risk of public health problems. Planting vegetation might be used to capture the fine particles at the roadside, which can lower the health risks for the urban population. Istanbul is the most populous city in Turkey, where the number of non-electric cars is increasing rapidly, resulting in decreasing air quality, especially at the roadsides. Recent studies show that cardiovascular, respiratory and total non-accidental mortality is increasing with short-term exposure to outdoor air pollution in Istanbul. In this study, roadside trees were investigated for the mitigation effect on vehicle-related PM2.5 and heavy metal (HM). Cupressus sempervirens (Mediterranean Cypress trees) were planted in three different cases (i.e., no trees-C1, trees with gaps-C2 and thick trees with no gaps-C3) at the study site. Location of the site is on a dense-traffic roadside in Istanbul, where thousands of people are living, working and walking through two sides of this road. PM2.5 samples and tree leaves were examined in the performed experiments. C2 and C3 showed the importance of roadside tree plantation by reducing the exposure to significantly low levels. Roadside PM2.5 concentrations were reduced by 17% in C3, equivalent to urban background levels in the city. Maximum removal of HMs is observed in nickel from 26.4 ± 7.8 to 7.5 ± 2.4 μg m−3. Pedestrian exposure is calculated with the measured data in three experiments and exposure is significantly reduced (e.g., >50% for cadmium and lead exposure) in experiment C3. In conclusion, three experiments showed that Mediterranean Cypress trees significantly decreased particle pollution at roadsides in Istanbul.

Effects of Meteorological Parameters on Gaseous Air Pollutant Concentrations in Urban Area of Gwalior City, India

This study offers a complete urban air assessment taking into account the weather conditions and pollution sources. An assessment of meteorological conditions affecting the air quality and correlation among concentrations of pollutants and meteorological factors was done. The correlation between temperature, relative humidity, wind speed and sulfur dioxide and nitrogen dioxide concentrations was studied separately from the analysis of the monthly concentrations of these gaseous pollutants. Four urban areas of Gwalior were selected for this study to represent the city. For sampling and assessment, the guidelines of central pollution control board India were followed.

A novel approach to evaluate the lung cancer risk of airborne particles emitted in a city

Air quality still represents a main threat to human health in cities. Even in developed countries, decades of air pollution control not yet allowed to reduce pollutant concentrations in urban areas adequately. Indeed, high airborne particle concentrations are measured in several European cities; this is a main issue since particles represent a carrier for carcinogenic compounds. Numerous researches measuring the exposure to the different aerosol metrics in urban areas were recently performed, nonetheless, few data on the lung cancer risk in such environments are available.In the present paper a novel approach to evaluate the lung cancer risk related to the airborne particles emitted by the different sources located in a city is proposed and applied to a pilot case-study (i.e. an Italian city). In particular, an existing lung cancer risk model was modified and applied to assess the particle-related lung cancer “emitted” by the different sources of the city using pollutant emission factors provided by accredited emission inventory databases. Therefore, the average toxicity of the particles emitted by the city (i.e. lung cancer slope factor) and the lung cancer risk globally emitted by the city, expressed as new cases of lung cancer, were evaluated.The proposed emission inventory also allowed to identify and localize the main contributors to the overall risk emitted in a city. As an example, for the city under investigation, the research revealed that the main contributor, amongst the sources considered, is the vehicular traffic which is characterized by a lower mass fraction of carcinogenic compounds but a much higher sub-micron particle emission with respect to the other sources.

Primary effects of changes in meteorology vs. anthropogenic emissions on mercury wet deposition: A modeling study

In this study we investigated the respective contributions of changing meteorology and anthropogenic emissions to Hg wet deposition in New York State (NYS), which illustrated quantitatively how the variations in meteorological conditions modified the response of Hg wet deposition to anthropogenic emission reductions in NYS. A regional chemical transport model, CMAQ-newHg-Br (Ye et al., 2018), was employed driven by four sets of meteorological fields (2004, 2005, 2007, and 2010) and two anthropogenic emission inventories (NEI, 2005 and 2011). Four major findings emerged from this study. First, simulated Hg wet deposition flux in NYS was greatly affected by variation in meteorological conditions, ranging from a 91% decrease to a factor of 5 increase in monthly Hg wet deposition, whereas it hardly responded (2%) to a near doubling of emissions in the northeastern U.S. Second, the precipitation amount dominated interannual and seasonal variations of Hg wet deposition over all other meteorological variables, with total (= convective + non-convective) precipitation explained 60% of Hg wet deposition variance in an urban environment compared to convective precipitation alone explaining 65% in a rural area. Third, large-scale atmospheric circulation influenced regional transport greatly leading to out-of-state anthropogenic emissions explaining at least 80% of Hg wet deposition changes in a NYS urban environment. Fourth, higher Hg concentrations in precipitation and higher atmospheric reactive mercury concentrations in urban areas were related to calm conditions due likely to regional build-up of emissions, whereas in rural areas photochemical oxidation of gaseous elemental mercury was a more important source of reactive mercury. Solar radiation variance was found to explain 41–55% of the variance in simulated reactive mercury concentrations. Given that more than 95% of the US is rural lands and the majority of water bodies occur in rural areas, a realistic chemical mechanism for photochemical gaseous elemental mercury oxidation is paramount to accurate simulation of reactive mercury formation and Hg wet deposition in the US.