Abstract. The Kathmandu Valley in south Asia is considered as one of the global "hot spots" in terms of urban air pollution. It is facing severe air quality problems as a result of rapid urbanization and land use change, socioeconomic transformation, and high population growth. In this paper, we present the first full year (February 2013–January 2014) analysis of simultaneous measurements of two short-lived climate forcers/pollutants (SLCF/P), i.e., ozone (O3) and equivalent black carbon (hereinafter noted as BC) and aerosol number concentration at Paknajol, in the city center of Kathmandu. The diurnal behavior of equivalent BC and aerosol number concentration indicated that local pollution sources represent the major contributions to air pollution in this city. In addition to photochemistry, the planetary boundary layer (PBL) and wind play important roles in determining O3 variability, as suggested by the analysis of seasonal changes of the diurnal cycles and the correlation with meteorological parameters and aerosol properties. Especially during pre-monsoon, high values of O3 were found during the afternoon/evening. This could be related to mixing and entrainment processes between upper residual layers and the PBL. The high O3 concentrations, in particular during pre-monsoon, appeared well related to the impact of major open vegetation fires occurring at the regional scale. On a synoptic-scale perspective, westerly and regional atmospheric circulations appeared to be especially conducive for the occurrence of the high BC and O3 values. The very high values of SLCF/P, detected during the whole measurement period, indicated persisting adverse air quality conditions, dangerous for the health of over 3 million residents of the Kathmandu Valley, and the environment. Consequently, all of this information may be useful for implementing control measures to mitigate the occurrence of acute pollution levels in the Kathmandu Valley and surrounding area.
Abstract Ozone concentration measurements were made during December from 2001–2005 to quantify the contributions of different processes to near-surface ozone concentrations (O 3 ) in Terra Nova Bay, Antarctica. The average O 3 concentration was 20.3 ppbv. On days characterized by high solar radiation fluxes (HSR), significantly higher concentrations of O 3 (21.3 ppbv) were recorded compared to days with low solar radiation fluxes (LSR days, 16.8 ppbv). High O 3 concentrations could be related to strong winds from SW–NW. Three-dimensional back-trajectories show that air from the interior of the continent could affect O 3 at Terra Nova Bay. Moreover, during HSR days, high O 3 concentrations were also recorded in connection with weak circulation, suggesting that emissions from the Italian base (located 2 km north) could also represent a significant source of O 3 . To clarify the role of local pollution in Terra Nova Bay, O 3 values were also calculated using the photochemical steady state (PSS) approximation under clear sky and cloudy conditions.
Abstract. Stratospheric intrusions (SI) are a topic of ongoing research, especially because of their ability to change the oxidation capacity of the troposphere and their contribution to tropospheric ozone levels. In this work, a novel tool called STEFLUX is presented, discussed and used to provide a first long-term investigation of SI over two global hot-spot regions for climate change and air pollution: the southern Himalayas and the central Mediterranean basin. The main purpose of STEFLUX is to obtain a fast-computing and reliable identification of the SI occurring at a specific location and during a specified time window. It relies on a compiled stratosphere-to-troposphere exchange (STE) climatology, which makes use of the ERA-Interim reanalysis dataset from the ECMWF, as well as a refined version of a well-established Lagrangian methodology. STEFLUX results are hereby compared to the SI observations (SIO) at two high-mountain WMO/GAW global stations in these climate hot-spots, i.e., the Nepal Climate Observatory-Pyramid (NCO-P, 5079 m a.s.l.) and Mt. Cimone (2165 m a.s.l.), which are often affected by SI events. Compared to the observational datasets at the two specific measurement sites, STEFLUX is able to detect SI on a regional scale. Furthermore, it has the advantage of retaining additional information concerning the pathway of stratospheric-affected air-masses, such as the location of tropopause crossing and other meteorological parameters along the trajectories. However, STEFLUX neglects mixing and dilution that air-masses undergo along their transport within the troposphere. Therefore, the regional-scale STEFLUX events cannot be expected to perfectly reproduce the point measurements at NCO-P and Mt. Cimone, which are also affected by small-scale (orographic) circulations. Still, the SI seasonal variability according to SI and STEFLUX agree fairly well. By exploiting the fact that the ERA-Interim reanalysis extends back to 1979, the long-term climatology of SI at NCO-P and Mt. Cimone is also assessed in this work. The analysis of the 35-year record at both stations denies the existence of any significant trend in the SI frequency, except for winter seasons at NCO-P. Furthermore, for the first time, by using the STEFLUX outputs, we investigate the potential impact of specific climate factors (i.e. ENSO, QBO and solar activity) on SI frequency variability over the Mediterranean basin and the Himalayas.
Abstract. The identification of spikes (i.e. short and high variability in the measured signals due to very local emissions occurring in the proximity of a measurement site) is of interest when using continuous measurements of atmospheric greenhouse gases (GHGs) in different applications like the determination of long-term trends and/or spatial gradients, the inversion experiments devoted to the top-down quantification of GHG surface-atmosphere fluxes, the characterization of local emissions or the quality control of GHG measurements. In this work, we analysed the results provided by two automatic spike identification methods (i.e. the standard deviation of the background - SD and the robust extraction of baseline signal - REBS) for a 2-year dataset of 1-minute in-situ observations of CO2, CH4 and CO at ten different atmospheric sites spanning different environmental conditions (remote, continental, urban). The sensitivity of the spike detection frequency and its impact on the averaged mole fractions on method parameters was investigated. Results for both methods were compared and evaluated against manual identification of the site Principal Investigators (PIs). The study showed that, for CO2 and CH4, REBS identified a larger number of spikes than SD and it was less “site-sensitive” than SD. This led to a larger impact of REBS on the time-averaged values of the observed mole fractions for CO2 and CH4. Further, it could be shown that it is challenging to identify one common algorithm/configuration for all the considered sites: method-dependent and setting-dependent differences in the spike detection were observed as a function of the sites, case studies and considered atmospheric species. Neither SD nor REBS appeared to provide a perfect identification of the spike events. The REBS tendency to over-detect the spike occurrence shows limitations when adopting REBS as an operational method to perform automatic spike detection. REBS should be used only for specific sites, mostly affected by frequent very nearby local emissions. SD appeared to be more selective in identifying spike events and the temporal variabilities of CO2, CH4 and CO were more consistent with that of the original datasets. Further activities are needed for better consolidating the fitness for purposes of the two proposed methods and to compare them with other spike detection techniques.
Abstract. The identification of spikes (i.e. short and high variability in the measured signals due to very local emissions occurring in the proximity of a measurement site) is of interest when using continuous measurements of atmospheric greenhouse gases (GHGs) in different applications like the determination of long-term trends and/or spatial gradients, the inversion experiments devoted to the top-down quantification of GHG surface-atmosphere fluxes, the characterization of local emissions or the quality control of GHG measurements. In this work, we analysed the results provided by two automatic spike identification methods (i.e. the standard deviation of the background - SD and the robust extraction of baseline signal - REBS) for a 2-year dataset of 1-minute in-situ observations of CO2, CH4 and CO at ten different atmospheric sites spanning different environmental conditions (remote, continental, urban). The sensitivity of the spike detection frequency and its impact on the averaged mole fractions on method parameters was investigated. Results for both methods were compared and evaluated against manual identification of the site Principal Investigators (PIs). The study showed that, for CO2 and CH4, REBS identified a larger number of spikes than SD and it was less “site-sensitive” than SD. This led to a larger impact of REBS on the time-averaged values of the observed mole fractions for CO2 and CH4. Further, it could be shown that it is challenging to identify one common algorithm/configuration for all the considered sites: method-dependent and setting-dependent differences in the spike detection were observed as a function of the sites, case studies and considered atmospheric species. Neither SD nor REBS appeared to provide a perfect identification of the spike events. The REBS tendency to over-detect the spike occurrence shows limitations when adopting REBS as an operational method to perform automatic spike detection. REBS should be used only for specific sites, mostly affected by frequent very nearby local emissions. SD appeared to be more selective in identifying spike events and the temporal variabilities of CO2, CH4 and CO were more consistent with that of the original datasets. Further activities are needed for better consolidating the fitness for purposes of the two proposed methods and to compare them with other spike detection techniques.
Abstract. The identification of spikes (i.e. short and high variability in the measured signals due to very local emissions occurring in the proximity of a measurement site) is of interest when using continuous measurements of atmospheric greenhouse gases (GHGs) in different applications like the determination of long-term trends and/or spatial gradients, the inversion experiments devoted to the top-down quantification of GHG surface-atmosphere fluxes, the characterization of local emissions or the quality control of GHG measurements. In this work, we analysed the results provided by two automatic spike identification methods (i.e. the standard deviation of the background - SD and the robust extraction of baseline signal - REBS) for a 2-year dataset of 1-minute in-situ observations of CO2, CH4 and CO at ten different atmospheric sites spanning different environmental conditions (remote, continental, urban). The sensitivity of the spike detection frequency and its impact on the averaged mole fractions on method parameters was investigated. Results for both methods were compared and evaluated against manual identification of the site Principal Investigators (PIs). The study showed that, for CO2 and CH4, REBS identified a larger number of spikes than SD and it was less “site-sensitive” than SD. This led to a larger impact of REBS on the time-averaged values of the observed mole fractions for CO2 and CH4. Further, it could be shown that it is challenging to identify one common algorithm/configuration for all the considered sites: method-dependent and setting-dependent differences in the spike detection were observed as a function of the sites, case studies and considered atmospheric species. Neither SD nor REBS appeared to provide a perfect identification of the spike events. The REBS tendency to over-detect the spike occurrence shows limitations when adopting REBS as an operational method to perform automatic spike detection. REBS should be used only for specific sites, mostly affected by frequent very nearby local emissions. SD appeared to be more selective in identifying spike events and the temporal variabilities of CO2, CH4 and CO were more consistent with that of the original datasets. Further activities are needed for better consolidating the fitness for purposes of the two proposed methods and to compare them with other spike detection techniques.
Abstract. The present paper investigates the diurnal and seasonal variability of the aerosol total number concentration, number and volume size distribution between 10 nm and 10 μm, from a combination of a scanning mobility particle sizer (SMPS) and an optical counter (OPC), performed over a two-year period (January 2006–February 2008) at the Nepal Climate Observatory-Pyramid (NCO-P) research station, (5079 m a.s.l.). The annual average number concentration measured over the two-year period at the NCO-P is 860 cm−3. Total concentrations show a strong seasonality with maxima during pre-monsoon and post-monsoon seasons and minima during the dry and monsoon seasons. A diurnal variation is also clearly observed, with maxima between 09:00 and 12:00 UTC. The aerosol concentration maxima are mainly due to nucleation processes during the post-monsoon season, as witnessed by high nucleation mode integrated number concentrations, and to transport of high levels of pollution from the plains by valley breezes during the pre-monsoon season, as demonstrated by high accumulation mode integrated number concentrations. Night-time number concentration of particles (from 03:00 to 08:00 NST) are relatively low throughout the year (from 450 cm−3 during the monsoon season to 675 cm−3 during the pre-monsoon season), indicating the of high altitudes background level, as a result of downslope winds during this part of the day. However, it was found that these background concentrations are strongly influenced by the daytime concentrations, as they show the same seasonal variability. If nighttime concentrations were presumed to be representative of free troposphere (FT)/residual layer concentrations, they would be found to be two times higher than at other lower altitudes European sites, such as the Jungfraujoch. However, BL intrusions might contaminate the free troposphere/residual layer even at this altitude, especially during regional air masses influence. Night-time measurements were subsequently selected to study the FT composition according to different air masses, and the effect of long range transport to the station.
This paper presents a detailed review of atmospheric pollution observed in the Hindu Kush–Himalaya (HKH) region and its implications for regional climate. Data from in situ measurements made at high-altitude stations in the HKH region, observations from satellite-based instruments, and global climate modeling study results are discussed. Experimental observations discussed include both atmospheric measurements and data from snow and ice core sampling from different glaciers in the HKH region. The paper focuses on the atmospheric brown cloud loadings over the Himalayas, particularly black carbon (BC) and ozone, which have links to regional climate and air-pollution–related impacts. Studies show elevated levels of anthropogenic ozone and BC over the Himalayas during the pre-monsoon season with concentrations sometimes similar to those observed over an average urban environment. The elevated concentration observed over the Himalayas is thought to come from the lowlands, especially the highly populated areas of the Indo-Gangetic Plains. The implications of high BC loading in the Himalayan atmosphere as well as elevated BC deposition on snow and ice surfaces for regional climate, hydrological cycle, and glacial melt are discussed.
