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Ozone in the troposphere affects humans and ecosystems as a pollutant and as a greenhouse gas. Observing, understanding and modelling this dual role, as well as monitoring effects of international regulations on air quality and climate change, however, challenge measurement systems to operate at opposite ends of the spatio-temporal scale ladder. Aboard the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite launched in October 2017, the TROPOspheric Monitoring Instrument (TROPOMI) aspires to take the next leap forward by measuring ozone and its precursors at unprecedented horizontal resolution until at least the mid-2020s. In this work, we assess the quality of TROPOMI's first release (V01.01.05–08) of tropical tropospheric ozone column (TrOC) data. Derived with the convective cloud differential (CCD) method, TROPOMI daily TrOC data represent the 3 d moving mean ozone column between the surface and 270 hPa under clear-sky conditions gridded at 0.5 ∘ latitude by 1 ∘ longitude resolution. Comparisons to almost 2 years of co-located SHADOZ ozonesonde and satellite data (Aura OMI and MetOp-B GOME-2) conclude to TROPOMI biases between - 0.1 and + 2.3 DU (<+13 %) when averaged over the tropical belt. The field of the bias is essentially uniform in space (deviations <1 DU) and stable in time at the 1.5–2.5 DU level. However, the record is still fairly short, and continued monitoring will be key to clarify whether observed patterns and stability persist, alter behaviour or disappear. Biases are partially due to TROPOMI and the reference data records themselves, but they can also be linked to systematic effects of the non-perfect co-locations. Random uncertainty due to co-location mismatch contributes considerably to the 2.6–4.6 DU (∼14 % –23 %) statistical dispersion observed in the difference time series. We circumvent part of this problem by employing the triple co-location analysis technique and infer that TROPOMI single-measurement precision is better than 1.5–2.5 DU (∼8 % –13 %), in line with uncertainty estimates reported in the data files. Hence, the TROPOMI precision is judged to be 20 % –25 % better than for its predecessors OMI and GOME-2B, while sampling at 4 times better spatial resolution and almost 2 times better temporal resolution. Using TROPOMI tropospheric ozone columns at maximal resolution nevertheless requires consideration of correlated errors at small scales of up to 5 DU due to the inevitable interplay of satellite orbit and cloud coverage. Two particular types of sampling error are investigated, and we suggest how these can be identified or remedied. Our study confirms that major known geophysical patterns and signals of the tropical tropospheric ozone field are imprinted in TROPOMI's 2-year data record. These include the permanent zonal wave-one pattern, the pervasive annual and semiannual cycles, the high levels of ozone due to biomass burning around the Atlantic basin, and enhanced convective activity cycles associated with the Madden–Julian Oscillation over the Indo-Pacific warm pool. TROPOMI's combination of higher precision and higher resolution reveals details of these patterns and the processes involved, at considerably smaller spatial and temporal scales and with more complete coverage than contemporary satellite sounders. If the accuracy of future TROPOMI data proves to remain stable with time, these hold great potential to be included in Climate Data Records, as well as serve as a travelling standard to interconnect the upcoming constellation of air quality satellites in geostationary and low Earth orbits. [ABSTRACT FROM AUTHOR]

Ozone in the troposphere affects humans and ecosystems as a pollutant and as a greenhouse gas. Observing, understanding and modelling this dual role, as well as monitoring effects of international regulations on air quality and climate change, however, challenge measurement systems to operate at opposite ends of the spatio-temporal scale ladder. On board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite launched in October 2017, TROPOspheric Monitoring Instrument (TROPOMI) aspires to take the next leap forward by measuring ozone and its precursors at unprecedented horizontal resolution until at least the mid 2020s. In this work, we assess the quality of TROPOMI's first release (V01.01.05-08) of tropical tropospheric ozone column data (TrOC). Derived with the Convective Cloud Differential (CCD) method, TROPOMI daily TrOC data represent the three-day moving mean ozone column between surface and 270 hpa under clear sky conditions gridded at 0.5° latitude by 1° longitude resolution. Comparisons to almost two years of co- located SHADOZ ozonesonde and satellite data (Aura OMI and MetOp-B GOME-2) conclude to TROPOMI biases between -0.1 and +2.3 DU (< +13 %) when averaged over the tropical belt. The field of the bias is essentially uniform in space (deviations < 1 DU) and stable in time at the 1.5-2.5 DU level. However, the record is still fairly short and continued monitoring will be key to clarify whether observed patterns and stability persist, alter behaviour or disappear. Biases are partially due to TROPOMI and the reference data records themselves, but they can also be linked to systematic effects of the non perfect co-locations. Random uncertainty due to co-location mismatch contributes considerably to the 2.6-4.6 DU (~14-23 %) statistical dispersion observed in the difference time series. We circumvent part of this problem by employing the triple co-location analysis technique and infer that TROPOMI single-measurement precision is better than 1.5-2.5 DU (~8-13 %), in line with uncertainty estimates reported in the data files. Hence, the TROPOMI precision is judged to be 20-25 % better than for its predecessors OMI and GOME- 2B, while sampling at four times better spatial resolution and almost twice better temporal resolution. Using TROPOMI tropospheric ozone columns at maximal resolution nevertheless requires consideration of correlated errors at small scales of up to 5 DU due to the inevitable interplay of satellite orbit and cloud coverage. Two particular types of sampling error are investigated and we suggest how these can be identified or remedied. Our study confirms that major known geophysical patterns and signals of the tropical tropospheric ozone field are imprinted in TROPOMI's two- year data record. These include the permanent zonal wave-one pattern, the pervasive annual and semiannual cycles, the high levels of ozone due to biomass burning around the Atlantic basin, and enhanced convective activity cycles associated with the Madden- Julian Oscillation over the Indo-Pacific warm pool. A quasi-periodic signal of 1-2 weeks and 3-5 DU amplitude in TrOC time series, especially at low latitudes, is reminiscent of Kelvin wave activity. TROPOMI's combination of higher precision and higher resolution reveal details of these patterns and the processes involved, at considerably smaller spatial and temporal scales and with more complete coverage than contemporary satellite sounders. If the accuracy of future TROPOMI data proves to remain stable with time, these hold great potential to be included in Climate Data Records, as well as serve as a travelling standard to interconnect the upcoming constellation of air quality satellites in geostationary and low Earth orbits. [ABSTRACT FROM AUTHOR]

This study investigates the fine particulate matter (PM2.5) variability in the Klang Valley urban-industrial environment. In total, 94 daily PM2.5 samples were collected during a 1-year campaign from August 2011 to July 2012. This is the first paper on PM2:5 mass, chemical composition and sources in the tropical environment of Southeast Asia, covering all four seasons (distinguished by the wind flow patterns) including haze events. The samples were analysed for various inorganic components and black carbon (BC). The chemical compositions were statistically analysed and the temporal aerosol pattern (seasonal) was characterised using descriptive analysis, correlation matrices, enrichment factor (EF), stoichiometric analysis and chemical mass closure (CMC). For source apportionment purposes, a combination of positive matrix factorisation (PMF) and multi-linear regression (MLR) was employed. Further, meteorological-gaseous parameters were incorporated into each analysis for improved assessment. In addition, secondary data of total suspended particulate (TSP) and coarse particulate matter (PM10) sampled at the same location and time with this study (collected by Malaysian Meteorological Department) were used for PM ratio assessment. The results showed that PM2:5 mass averaged at 28±18 μgm-3, 2.8-fold higher than the World Health Organisation (WHO) annual guideline. On a daily basis, the PM2.5 mass ranged between 6 and 118 μg m-3 with the daily WHO guideline exceeded 43% of the time. The north-east (NE) monsoon was the only season with less than 50% sample exceedance of the daily WHO guideline. On an annual scale, PM2.5 mass correlated positively with temperature (T ) and wind speed (WS) but negatively with relative humidity (RH). With the exception of NOx , the gases analysed (CO, NO2, NO and SO2) were found to significantly influence the PM2.5 mass. Seasonal variability unexpectedly showed that rainfall, WS and wind direction (WD) did not significantly correlate with PM2.5 mass. Further analysis on the PM2.5 =PM10, PM2.5 = TSP and PM10 = TSP ratios reveal that meteorological parameters only greatly influenced the coarse particles (particles with an aerodynamic diameter of greater than 2.5 μm) and less so the fine particles at the site. Chemical composition showed that both primary and secondary pollutants of PM2:5 are equally important, albeit with seasonal variability. The CMC components identified were in the decreasing order of (mass contribution) BC> secondary inorganic aerosols (SIA) > dust > trace elements > sea salt >K+. The EF analysis distinguished two groups of trace elements: those with anthropogenic sources (Pb, Se, Zn, Cd, As, Bi, Ba, Cu, Rb, V and Ni) and those with a crustal source (Sr, Mn, Co and Li). The five identified factors resulting from PMF 5.0 were (1) combustion of engine oil, (2) mineral dust, (3) mixed SIA and biomass burning, (4) mixed traffic and industrial and (5) sea salt. Each of these sources had an annual mean tribution of 17, 14, 42, 10 and 17% respectively. The dominance of each identified source largely varied with changing season and a few factors were in agreement with the CMC, EF and stoichiometric analysis, accordingly. In relation to meteorological-gaseous parameters, PM2.5 sources were influenced by different parameters during different seasons. In addition, two air pollution episodes (HAZE) revealed the influence of local and/or regional sources. Overall, our study clearly suggests that the chemical constituents and sources of PM2.5 were greatly influenced and characterised by meteorological and gaseous parameters which vary greatly with season. [ABSTRACT FROM AUTHOR]