Heating Rate of Light Absorbing Aerosols: Time-Resolved Measurements, the Role of Clouds, and Source Identification

Light absorbing aerosols (LAA) absorb sunlight and heat the atmosphere. This work presents a novel methodology to experimentally quantify the heating rate (HR) induced by LAA into an atmospheric layer. Multiwavelength aerosol absorption measurements were coupled with spectral measurements of the direct, diffuse and surface reflected radiation to obtain highly time-resolved measurements of HR apportioned in the context of LAA species (black carbon, BC; brown carbon, BrC; dust), sources (fossil fuel, FF; biomass burning, BB), and as a function of cloudiness. One year of continuous and time-resolved measurements (5 min) of HR were performed in the Po Valley. We experimentally determined (1) the seasonal behavior of HR (winter 1.83 ± 0.02 K day^–1; summer 1.04 ± 0.01 K day^–1); (2) the daily cycle of HR (asymmetric, with higher values in the morning than in the afternoon); (3) the HR in different sky conditions (from 1.75 ± 0.03 K day^–1 in clear sky to 0.43 ± 0.01 K day^–1 in complete overcast); (4) the apportionment to different sources: HR_FF (0.74 ± 0.01 K day^–1) and HR_BB (0.46 ± 0.01 K day^–1); and (4) the HR of BrC (HR_BrC: 0.15 ± 0.01 K day^–1, 12.5 ± 0.6% of the total) and that of BC (HR_BC: 1.05 ± 0.02 K day^–1; 87.5 ± 0.6% of the total)

Aerosol Corrosion Prevention and Energy-Saving Strategies in the Design of Green Data Centers

The energy demands of data centers (DCs) worldwide are rapidly increasing, as are their environmental and economic costs. This paper presents a study conducted at Sannazzaro de’ Burgondi (Po Valley), Italy, specifically aimed at optimizing the operating conditions of a DC designed for the Italian Oil and Gas Company (Eni) (5200 m² of Information Technology installed, 30 MW) and based on a direct free cooling (DFC) system. The aim of the study was to save the largest possible quantity of energy, while at the same time preventing aerosol corrosion. The aerosol properties (number size distribution, chemical composition, deliquescence relative humidity (DRH), acidity) and meteorological parameters were monitored and utilized to determine the potential levels of aerosol entering the DC (equivalent ISO class), together with its DRH. These data enabled us both to select the DC’s filtering system (MERV13 filters) and to optimize the cooling cycle through calculation of the most reliable humidity cycle (60% of maximum allowed RH) applicable to the DFC. A potential energy saving of 81%, compared to a traditional air conditioning cooling system, was estimated: in one year, for 1 kW of installed information technology, the estimated energy saving is 7.4 MWh, resulting in 2.7 fewer tons of CO₂ being emitted, and a financial saving of € 1100

Department and worker characteristics of the analyzed population.

<p>Data are median (IQR) and n (%) unless otherwise indicated.</p><p>TTH = tension-type headache, M = migraine, MP = myogenous neck/shoulder pain.</p

Effects of the intervention (change from baseline in the number of days with pain or drug consumption at month 7) on the study outcomes.

<p>*Adjusted by age, sex, neck/shoulder pain (when analyzing headache and analgesic drug consumption), headache (when analyzing neck/shoulder pain and analgesic drug consumption), education level, and job activity.</p

Comparison of the proportion of subjects with (“improved”^*) or without (“not improved” ^‡) reduction in pain frequency or drug consumption of ≥50% at the end of follow-up (responder rates), between IG and control group.

<p>*“Improved”: subjects with a baseline frequency of ≥4 days/month with pain (or drug consumption) that had a reduction in pain frequency or drug consumption of ≥50% at the end of follow-up.</p>‡<p>“Not improved”: includes subjects with ≥4 days/month with pain (or drug consumption) at the baseline with less than 50% of reduction in pain frequency or drug consumption at the end of follow-up, and those subjects that had a baseline frequency of less than 4 days with pain/drug consumption independently from their results.</p>§<p>adjusted by age, sex, neck/shoulder pain (when analyzing headache and analgesic drug consumption), headache (when analyzing neck/shoulder pain and analgesic drug consumption), education level, job activity and baseline value of each subject.</p

Mean differences (days/month) between groups in the changes from baseline (month 7 vs. month 1) of the frequency of headache (panel A), neck/shoulder pain (panel B), headache and/or neck/shoulder pain (panel C), by subgroups.

<p>Mean differences (days/month) between groups in the changes from baseline (month 7 vs. month 1) of the frequency of headache (panel A), neck/shoulder pain (panel B), headache and/or neck/shoulder pain (panel C), by subgroups.</p

Flow chart.

<p>Flow chart.</p

Results (responder rates) of the sensitivity analyses performed on the whole randomized population according to two different <i>scenarios</i>.

<p>*Scenario 1: the probability of improvement observed in the control group was assigned to the workers not completing the baseline and/or the follow-up diary in both the IG and the control group.</p><p>**Scenario 2 (<i>worst scenario</i>): the probability of improvement observed in the IG was assigned to the workers not completing the baseline and/or the follow-up diary in the control group, whereas the probability of improvement observed in the control group was assigned to the workers not completing the baseline and/or the follow-up diary in the IG.</p