Cumulative effect of PM_2.5 components is larger than the effect of PM_2.5 mass on child health in India

Domain: earth_science
Published: 2023-10-31
Editorial outcome: major_revision

Abstract
Whilestudiesonambientfineparticulatematter(PM )exposureeffecton
2.5
childhealthareavailable,thedifferentialeffects,ifany,ofexposuretoPM
2.5
speciesareunexploredinlowerandmiddle-incomecountries.Usingmultiple
logisticregression,weshowedthatforevery10μgm−3increaseinPM
2.5
exposure,anaemia,acuterespiratoryinfection,andlowbirthweightpre-
valenceincreaseby10%(95%uncertaintyinterval,UI:9–11),11%(8–13),and5%
(4–6),respectively,amongchildreninIndia.NO -,elementalcarbon,andNH +
3 4
weremoreassociatedwiththethreehealthoutcomesthanotherPM species.
2.5
WefoundthatthetotalPM massasasurrogatemarkerforairpollution
2.5
exposurecouldsubstantiallyunderestimatethetruecompositeimpactof
differentcomponentsofPM .Ourfindingsprovidekeyindigenousevidence
2.5
toprioritizecontrolstrategiesforreducingexposuretomoretoxicspeciesfor
greaterchildhealthbenefitsinIndia.

Introduction
Airpollutionposesasignificantglobalhealthrisk,with6.67million evenatlowlevels,particularlyduringfoetaldevelopmentandthefirst
(95%UI: 5.90–7.49) deaths worldwide attributable to the combined fewmonthsoflife6.Thus,early-lifeexposuretoairpollution7impacts
effectsofhouseholdandambientairpollution1.In2019,over99%of childhealthoutcomessuchasacuterespiratoryinfection(ARI)8,which
the world’s population inhaled fine particulate matter (PM ) con- are the most common cause of global childhood morbidity and
2.5
centrations that exceeded the World Health Organization (WHO) mortality9.Inadditiontomortality,ambientPM exposureisarisk
2.5
annualairqualityguideline(AQG)of5μgm−32.InIndia,hometoone- factor for adverse pregnancy outcomes, including low birth weight
sixthoftheglobalpopulation,ambientPM exposurehasbeenrising (LBW)10, stillbirth11, foetal mortality12, preterm birth13, and birth
2.5
inthelastthreedecades3.In2017,themajority(76.8%)ofthepeoplein defects14.Inearlychildhood,itisariskfactorforimpairedchildgrowth,
India were exposed to an annual population-weighted mean PM stunting6,15, and anaemia16,17. Since the lower and middle-income
2.5
higher than the national ambient air quality standard (NAAQS) of countries(LMICs)havehigherlevelsofambientPM exposurecom-
2.5
40μgm−34.
paredtohigher-incomecountries;theLMICchildrenarelikelytohave
The latest round of the state-level burden of diseases in India disproportionately higher rates of these morbidities and mortality
estimatedthatambientPM exposurewasresponsiblefor0.98mil- linkedtoprenatalandearlychildhoodexposuretoambientPM 18.
2.5 2.5
lion(0.77–1.19)deathsand17.8milliondisability-adjustedlifeyears5. PM ,byitself,isacompositemixtureofmultiplechemicalspe-
2.5
TheLancetCommissionhasemphasizedthatchildrenundertheageof cies.ThecomponentsofPM mayhavevaryingtoxicitywithvarying
2.5
5years(U5)aremorevulnerabletotheharmfulimpactsofairpollution, massfractions.Asaresult,thetruecumulativeimpactofambientPM
2.5
onhealthcouldbemanifoldhigherthanthatestimatedbythetotal prevalencewasrelativelyhigherinruralareasthaninurban(2.9%vs.
PM mass19,20.Moreover,thePM speciescanbetaggedtospecific 2.4%)areas(SupplementaryTable1).
2.5 2.5
emitting sectors, providing opportunities to examine their relative TheannualaveragePM levelatthePSUlevelwas62µgm−3with
2.5
importance in mitigation strategies. The association between PM aninterquartilerange(IQR)of52–79µgm−3.ThemostdominantPM
2.5 2.5
components and their sectoral contributions and U5 child health componentswereorganiccarbon(OC),NO -,NH +,SO 2-,andothers
3 4 4
outcomesisunknownatthenationallevelintheLMICs.
thatincludechloride,sodium,magnesium,potassium,calcium,soil,
To address these critical knowledge gaps, we estimated the and water molecules, and the remaining unspecified components.
associationofambientPM anditscomponentsandemittingsectors These were mostly contributed from domestic, industrial, interna-
2.5
onthreeU5childhealthoutcomes—anaemia,ARI,andLBW,inIndia tional,agricultural,andtransportsectors(SupplementaryTable2).

Results
Studypopulationandcharacteristics
There were 259,627 observations in the original NFHS-4 dataset, of
which15,119childrenhadmissingage.Afterremovingmissingrecords
of exposure, outcome, and covariates, there were 177,072 observa-
tionsinthefinalanalyticalsampleforLBWandARI(adetailedflow-
chartisgiveninSupplementaryFig.1).Intheanaemiaanalysis,allthe
anaemiastatus-missingobservationswereexcluded.
ThenationalprevalenceofLBWfromtheanalyticalsamplewas
16.6%(16.4,16.8).Thiswasslightlyhigheringirls(17.8%)thaninboys
(15.5%).AsignificantvariationintheprevalenceofLBWwasobserved
across the levels of the mother’s education, socioeconomic status,
mother’sbodymassindex(BMI),age,placeofresidence,anddifferent
levels of PM . The prevalence of anaemia among U5 children was
2.5
56.8%(56.6,57.1).Anaemiawasassociatedwithmaternaleducation,
religion,socioeconomic,placeofresidence,levelsofPM exposure,
2.5
andmaternalanaemiastatus.
ThenationalprevalenceofARIwas2.8%(2.7–2.9).Maternaledu-
cation,religion,socioeconomic,placeofresidence,andlevelsofPM
2.5
exposure were significantly associated with ARI. The estimated ARI
EffectsofPM anditscomponentsandsourcesonchildhealth
2.5
Thetwo-stagemodel(‘Methods’)estimatedtheoddsratio,ORofLBW
as 1.15 (1.12–1.18), of anaemia as 1.57 (1.54–1.59), and of ARI as 1.32
(1.24–1.4)foreveryIQRincreaseinambientPM exposure.Further,
2.5
gestationalexposuretoNO -showedthehighestassociationonLBW
3
ofchildrenwithOR1.17(1.14–1.2)perIQRincreaseinexposure.The
estimatedORsofLBWforeveryIQRincreaseingestationalexposure
ofothers,NH +,elementalcarbon(EC),soil,SO 2-,andOCwere1.14
4 4
(1.11–1.17), 1.13 (1.11–1.16), 1.11 (1.08–1.14), 1.09 (1.07–1.11), 1.07
(1.04–1.09),and1.05(1.03–1.08),respectively(Fig.1).Foranaemia,we
observedthelargestimpactofNO -(OR:1.36,1.32–1.41),followedby
3
NH + (OR: 1.28, 1.25–1.31), others (OR: 1.25, 1.21–1.28), EC (OR: 1.21,
4
1.18–1.25),soil(OR:1.18,1.16–1.20),SO 2-(OR:1.14,1.12–1.17),OC(OR:
4
1.12,1.09–1.15)(Fig.1).Similarly,theeffectofNO -onARIwasrelatively
3
higher than other components. The estimated OR of ARI was 1.52
(1.42–1.61)foreveryIQRincreaseinNO -.ECalsohadalargerimpact
3
onARI(OR:1.49,1.4–1.58),followedbyOC(OR:1.46,1.37–1.55),others
(OR:1.33,1.26–1.41)andNH +(OR:1.15,1.09–1.21)(Fig.1).
4
Among the eight PM sources analysed using the two-stage
2.5
model, we found that the IQR increase in PM from road dust
2.5
exhibitedahighereffectonLBW(OR:1.13,1.11–1.14),followedbythe
internationaltransboundarytransport(OR:1.09,1.07–1.1),theindustry
sector (OR: 1.07, 1.05–1.08), the agricultural sector (OR: 1.06,
1.05–1.07), other sectors (OR: 1.04, 1.02–1.07) and transport sector
(OR:1.05,1.02–1.07).Foranaemia,everyIQRincreaseinPM expo-
2.5
sure attributable to the sectoral emissions of unorganized (that
includesmunicipalwasteburningandcrematorium)sectors(OR,1.19;
1.18–1.20) showed the highest adverseeffect, followed by the inter-
nationaltransboundarytransport(OR:1.11,1.09–1.13),domesticand
road dust (ORs: 1.09 (1.06–1.11) and 1.09 (1.08–1.11), respectively),
agriculture(OR:1.08,1.07–1.09),industry(OR:1.04,1.02–1.06),and
transport (OR: 1.03, 1.0–1.06) sector. The power sector (OR: 0.96,
0.94–0.98) did not show any effect on anaemia among children
(Fig.2).ThedomesticsectorshowedthelargesteffectonARIthanthe
restofthesectors.ForeveryIQRincreaseofPM fromthedomestic
2.5
sector, we observed OR of ARI as 1.30 (1.24–1.35) followed by the
transportsector(OR:1.21,1.14–1.28),othersectors(OR:1.21,1.14–1.28)
andtheagriculturalsector(OR:1.1,1.07–1.13).

EffectsofPM componentsonanaemiaandARI
2.5
stratifiedbyLBW
Stratified estimateswereobtainedbyaddinganinteractiontermof
LBW in the second-stage model with air pollutants, one at a time
(‘Methods’). For an IQR increase in NO -, we observed the OR of
3
anaemia as 1.47 (1.41–1.52) for children born with LBW vs. 1.34
(1.3–1.38) for children born with normal weight. Apart fromNO -,
3
alargerdifferencewasobservedforEC(OR1.3vs.1.2),andOC(OR1.2vs.
1.1). Similarly, OR of ARI was 1.72 (1.6–1.85) and 1.46 (1.37–1.56),
respectively,forLBWandnormalbirthweightchildrenforIQR
increaseinNO .EC(OR1.7vs.1.4)andOC(OR1.6vs.1.4)alsoshowed
3
large differences between LBW and normal birth weight (Fig. 3).
ExactlysimilarpatternswereobservedforPM emissionattributable
2.5
todifferentsectorswhenstratifiedbyLBW(Fig.4).

Potentialnonlinearassociation
The exposure-response relationship of components OC, NO -, and
3
NH +exhibitedamonotonicincreaseuptoamidrangeconcentration
4
value,whereastherestofthecomponents,suchasEC,soil,andothers,
showedaninvertedU-shapedpatternforLBW(SupplementaryFig.2).
However, for anaemia(SupplementaryFig. 3), the majority of com-
ponents, such as EC, NH +, NO -, OC, SO 2-, and others, showed a
4 3 4
monotonicincreaseintheprobabilityofanaemiaprevalencewithan
increase in pollutant exposure. In contrast, for soil, the trend line
witnessedafall.Thenonlinearassociationbetweentheprobabilityof
anaemia prevalenceincreased with increasing PM exposure. Simi-
2.5
larly,varyingnonlinearpatternswereobservedforARI(Supplemen-
taryFig.4).

Cumulativeeffects
ThecumulativeeffectsofPM componentsonrespectivechildhealth
2.5
endpoints were estimated by summing over individual component-
specific estimates of regression coefficients (‘Methods’) adjusted to
theirrespectivemassfraction.Thecumulativeregressioncoefficient
wasdefinedas the

Xk
β^ cum= m i β^ i
i=1

where,m iwasthemassfractionsandβ^ iwastheestimatedregression
coefficient for ith comPponent. The standard error of β^ cum was esti-
mated by Vðβ^ cum Þ= k i=1 m2 i Vðβ^ i Þ assuming independence among
regressioncoefficientsofindividualcomponents.
ThecumulativeORofPM componentsfor10µgm−3increasein
2.5
PM masswasestimatedas1.23(1.21–1.26)forLBW,1.49(1.45–1.52)
2.5
for anaemia and 1.35 (1.29–1.41) for ARI, while for every 10µgm−3
increaseinPM mass,ORswereestimatedas1.05(1.04–1.06),1.10
2.5
(1.09–1.11),and1.11(1.08–1.13)forLBW,anaemia,andARI,respectively.

Expectedhealthbenefitsofmeetingcleanairtargets
Thenationalambientairqualitystandard(NAAQS)forannualPM
2.5
wassetat40μgm−3in2009bytheGovernmentofIndia21.Weexam-
inedtheexpectedhealthbenefitsofmeetingtheNAAQS(Fig.5b,e,h)
andeventuallytheWHO-AQG(Fig.5c,f,i)basedontheindigenous
exposure-responsefunctions.Wefirstestimatedtheexpectedreduc-
tioninambientPM exposure(4PM )atthedistrictlevelifIndia
2.5 2.5
successfullymeetstheNAAQSandthentheWHO-AQGrelativetothe
currentexposurelevel.Then,wecalculatedtheattributablefraction
(AF)asfollows:

ðRR(cid:2)1Þ
AF= 2.5
RR

whereRR=expðlogðORÞ×4PM 2:5 Þ,andORistheestimatedoddsratio
for each unit increase of PM exposure reported in previous
2.5
subsection(ORswereconvertedforunitincreaseofPM ).Finally,we
2.5
calculatedtheexpectedreductionindistrict-levelprevalence(E)for
LBW,anaemia,andARIduetothereductioninPM exposureas:
2.5
E=AF×Districtlevelprevalence

WhileweconsiderORforelevationofPM mass(i.e.,1.005,1.01
2.5
and 1.011 for LBW, anaemia and ARI, respectively) the overall LBW
prevalencecouldreducefrom16.6%(16.4,16.7)to14.5%(14.1,14.9)if
theNAAQSlevelisachieved.IftheexposureisreducedtotheWHO-
AQGlevel,theLBWprevalencecouldreduceto11.6%(11.1,12.4).For
anaemia,theprevalencecouldreducefrom56.8%(56.6,57.1)to44.8%
(43.8,45.7)andfurtherto32.9%(32.1,33.6),respectively,whileforARI,
theprevalencecouldreducefrom2.8%(2.7,2.9)to2.1%(1.9,2.3)and
further to 1.5% (1.3, 1.6) if the NAAQS and WHO-AQG levels are
achieved.
WhileweconsidercumulativeORforelevationofPM 2.5 compo-
nents(i.e.,1.021,1.041and1.03forLBW,anaemiaandARI,respectively,
the overallLBW prevalence could reduce from 16.6% (16.4, 16.7) to
15.7% (15.3, 16.1) if the NAAQS level is achieved. If the exposure is
reducedtotheWHO-AQGlevel,theLBWprevalencecouldreduceto
14.7% (14.3, 15.09). For anaemia, the prevalence could reduce from
56.8%(56.6,57.1)to50.7%(49.7,51.8)andfurtherto44.2%(43.3,45.1),
respectively,whileforARI,theprevalencecouldreducefrom2.8%(2.7,
2.9)to2.3%(2.1,2.7)andfurtherto2.1%(1.8,2.5)iftheNAAQSand
WHO-AQGlevelsareachieved.

Methods
Healthdata
The health dataset was obtained from Demographic Health Survey
(DHS) version seven https://www.dhsprogram.com/data/available-
datasets.cfm, which provides national-level health data for India as
theNFHS-434.NFHS-4wasahouseholdsurveyconductedbetween20
January2015and4December2016acrossall640districtsofIndia.The
surveydataprovidedthenecessaryinformationonhealthandfamily
welfare, along with details on current threats in these areas, to aid
policiesandprogrammesinIndia’shealthsectorovertime.Informa-
tion on socioeconomic status, reproductive health and family plan-
ning, maternal and child health, breastfeeding and nutrition,
vaccination coverage, anaemia, and the symptoms of ARI were col-
lectedinthesurvey.
TheNFHS-4samplewasatwo-stagestratifiedsample.Thesam-
plingframefortheselectionofPrimarySamplingUnits(PSU)wasthe
2011census.PSUsinruralareaswerevillages,andCensusEnumeration
Blocks(CEBs)wereinurbanareas.PSUswithlessthan40households
werecombinedwiththeclosestPSU.ProbabilityProportionaltoSize
(PPS)samplingwasusedtoselectthefinalPSUs.SelectedPSUswith
more than300households were divided into segments of 100–150
households,and twosegments were selected atrandomwith prob-
abilityproportionaltosegmentsize.Inthesecondstage,22house-
holdswereselectedfromeachruralandurbanclusterusingsystematic
sampling.Datacollectionwascarriedoutusingvariousquestionnaires.
Thesurveyincludedfourquestionnaires—ahouseholdquestionnaire,
a woman’s questionnaire, a man’s questionnaire, and a biomarker
questionnaire.Moredetailedinformationonthesampledesignand
selectionisavailableontheInternationalInstituteofPopulationSci-
encesNationalFamilyandHealthSurvey-4Report34.

AmbientPM exposuredata
2.5
OurprimaryexposuremetricwasambientPM exposure.Monthly
2.5
averagePM concentrationsatPSUlevelwereusedtoconstructlong-
2.5
term exposure for three different health outcomes. For low birth
weight,theaverageofmonthlyPM ambientconcentrationsatthe
2.5
PSUduringthepregnancywithnumberofmonthsbeingderivedbased
on individual gestational age was considered as pregnancy period
exposure. For anaemiaand ARI,the PM exposurewas derived by
2.5
averagingmonthlyambientPM concentrationatthePSUoverthe
2.5
periodofindividuallifecourse.Wetherefore,estimatedtheexposure
throughoutthegestationalperiodandlifecourseofU5childrenacross
all636districtsfromthenationalPM databasecreatedata1km×1
2.5
kmspatialscaleforIndia35.Thisdatabasewascreatedbyconverting
MODIS-MAIACaerosolopticaldepthtosurfacePM usingadynamic
2.5
scalingfactorfromMERRA-2reanalysisdata.TheinstantaneousPM
2.5
(representingthesatelliteoverpasstime)wasthenconvertedtoa24-h
average using the diurnal scaling factor from MERRA-2. Both these
scaling factors were calibrated against the data from the existing
ground-basednetworkoftheCentralPollutionControlBoardofIndia
(CPCB). At the annual scale, satellite-derived PM concentration
2.5
showedacorrelationcoefficientof0.97,andarootmeansquareerror
of 7.2µgm−3 with the coincident ground-based measurements from
theCPCBnetwork.TheIndividuallevelexposuretoambientPM was
2.5
calculatedbyclusteringtheexposureusinggeocodeinformationof
eachPSUintheNFHS-4data.

PM composition
2.5
SatellitedatacannotprovidedirectinformationonPM composition,
2.5
andverysparsegroundmeasurementsareavailableinIndia,thattoo
for a very limited duration. Therefore, we integrated satellite-PM
2.5
datawithoutputsfromapublishedstudywhichemployedWRFver-
sion 3.9.1-CMAQ version 5.3.1 setup to simulate ambient PM con-
2.5
centrationsat36×36km2 spatialresolutionwith25verticallevels33.
The modelling set-up (Supplementary Table 3) employed ERA5
meteorologyandemissionsestimatedusingGreenhouseGasandAir
Pollution Interactions and Synergies (GAINS)-ASIA model (https://
gains.iiasa.ac.at)inwhichgovernmentreportedenergyconsumption
dataforthedifferentsectorswasusedasaninputfortheyear2016.
ECLIPSE(version5)databaseofIIASA(2014)hadbeenusedforthe
nationalammoniaemissions,shipemissions,andtheemissionsemit-
ted from the neighbouring countries which fall within the study
domainthat,includesBhutan,Nepal,Myanmar,Bangladesh,SriLanka,
Pakistan, and parts of China and Afghanistan. In addition, trans-
boundary pollutants coming from outside the study domain were
taken from boundary conditions developed by the Community
AtmosphereModelwithChemistry(CAM-chem)model(https://www.
acom.ucar.edu/cam-chem/cam-chem.shtml). The ambient PM
2.5
simulated concentrations showed a significant agreement for the
coefficientofdeterminationwhencomparedwiththeobservedcon-
centrationsatground-basedmonitoringstations33.Thecoefficientof
determinationbetweentheobservedversussimulatedmonthlyaver-
agedconcentrationswasfoundtobe0.81,whiletheindexofagree-
mentwas0.94.
WeestimatedthemassconcentrationsofeachPM component
2.5
(Mi)as

Mi= PM M 2 i, : m 5, o m d o e d l el ×PM 2:5,satellite ð1Þ

whereMi,model andPM
2.5,model
arethemassofcomponent‘i’andtotal
PM mass derived from the model and PM is the satellite-
2.5 2.5,satellite
derivedPM .Sincethemodelestimateswereavailableat36km ×
2.5
36kmresolution,webilinearlyre-griddedthemodeldatato1km×1
km to match its resolution with the satellite-based PM dataset
2.5
resolution.Oncethegriddedmassconcentrationsofeachcomponent
wereestimated,wecalculatedtheexposurefortheNFHSclusters.

SectoralcontributiontoPM
2.5
The sectoral contributions to annual PM were estimated by the
2.5
subtraction method in the modelling framework. First, the control
simulationwascarriedoutwithallsectoralemissionson,andthenin
eachsubsequentsimulation,emissionsfromaparticularsectorwere
switchedoff,andthedifferenceprovidedthecontributionfromthat
sector.Themodeloutputs33wereanalysedtoderivePM contribu-
2.5
tions of the transport, small and medium-scale industries, brick
industry,majorindustries,power,domestic(duetosolidfuelusedfor
cookingandheating),agricultureresidueburning,construction,road
dust, and others (which includes refuse burning, construction, cre-
matoria, NH , biogenic emissions, refineries, and evaporative non-
3
methane volatile organic compounds). Here we used these model
outputsandcombinedthemwithsatellite-PM followingthemethod
2.5
explainedintheprevioussection(forPM components)toestimate
2.5
theexposuretoaspecificsectoralPM fortheNFHSclusters.
2.5

Outcomedata
ForU5children,theprimaryhealthoutcomesconsideredinthisstudy
were LBW, anaemia, and ARI. For LBW, the NFHS-4 recorded birth
weightfromeitherwrittenrecordsorthemother’soralreport.LBW
wasdefined bythe WHOas a birth weight<2500g36. Children with
LBWwerecodedas1,whereasthechildrenwithbirthweightgreater
than2500gwerecodedas0.
Information on anaemia prevalence among U5 children was
obtainedfromNFHS-4,whichusedthefingerorheelprickmethodto
collect blood samples.Haemoglobin concentrations were measured
on-site using the HemoCue Hb 201+ analyzer37. Children with hae-
moglobinlevels<11g/dLwereconsideredanaemic,andchildrenwith
haemoglobin levels >11g/dL were considered non-anaemic38. These
werecodedas1and0,respectively.
ARIinU5childrenwasdiagnosedbythereportingofsymptoms
likecough,accompaniedbyshortrapidbreathingand/ordifficultyin
breathingthatwasthoughttobechestrelated.Theinterviewerasked
motherswhethertheirchildrenexperiencedanyARIsymptomsinthe
two weeks preceding the survey. ARI was used as a dichotomous
variable with the presence of ARI symptoms coded as 1 and the
absenceofARIsymptomscodedas0.

Covariates
Severalindividual-levelandhousehold-levelvariableswereidentified
aspotentialcovariatesofLBW39,40,anaemia41andARI42.Thesevari-
ableswereincludedintheanalysisbasedontheirsignificancewith
the respective health outcome. The following individual-level vari-
ables were considered for all three outcome variables: sex of the
child(maleorfemale),mother’seducation(noeducation,primary,
secondary, and higher), and parity (1, or >1). In addition, for the
outcome variable ARI,weaccountedfortheageofthechild,forLBW,
mother’s age (<20, 20–35, >35) and body mass index (BMI) of the
mother (underweight, normal weight, overweight and obesity),
lastly,foranaemia,maternalhaemoglobinlevels,andpercapitairon
intake. Daily dietary iron intake (per capita) was obtained by con-
verting monthly food purchases capturedby the9th quinquennial
HouseholdConsumerExpendituresurveyofthe68throundofthe
NSSO43.Household-levelcovariatesusedforbothARIandanaemia
werethefollowing:socioeconomicstatus,whichisclassifiedintofive
wealth quintiles (poorest, poor, middle, rich, and richest), type of
residence (rural or urban), and passive smoking (yes or no). Since
studieshaveshownthatambient20andhousehold36airpollutionare
2.5
associatedwithanaemia,wealsoincludedthetypeofcookingfuelin
thehouseholdasacovariate.Thiswasclassifiedascleanfuel(elec-
tricity, LPG/natural gas, biogas), solid fuel (coal/lignite, charcoal,
wood,straw/shrubs/grass,agriculturalcropwaste,anddungcakes),
kerosene,andothers.ForLBW,onlythewealthindexwasusedasthe
household-levelcovariate.

Statisticalanalysis
GestationalperiodexposurewasassociatedwithLBW,andlifecourse
exposure was associated with ARI and anaemia. PSU level ambient
concentrationwasassignedastheexposuretoairpollutantsforchil-
drenresidingwithinthePSU.Toaccountforthisclustereffect,weused
alogisticmixedeffectsregressionmodel.Wealsowantedtoestimate
thedirecteffectsofallPM componentsadjustedforothercovari-
2.5
ates.OwingtohighcollinearityamongthePM components,weused
2.5
atwo-stagemodeltoavoidpotentialmulti-collinearity.Inthefirststep,
weregressedPM massconcentrationoneachcomponentatatime
2.5
andextractedtheresidualsasanalternativemetricthatcancapture
thetotalvariabilityofPM exceptvariabilityexplainedbythecom-
2.5
ponent.ThesePM -residualswerethenadjustedinthesecondstage
2.5
logisticmixedmodel.

Stage-I:

ResPM 2:5 i =PM 2:5 (cid:2)δ^ 0 (cid:2)δ^ 1 P i

Stage-II:

logit Prob Y ij=1ju i =β 0 +β 1 P i+β 2 ResPM 2:5 i +γðconfoundersÞ ij+u i

Whereδ^ andδ^ weretheestimatesofinterceptandslopefromthefirst
0 1
stage model. Y ij represented the binary outcome (anaemia/LBW/ARI)
forthejthindividualinithPSU,P iwasoneofthePM
2.5
components, (ResPM 2.5 )i was the residuals at ith PSU, u i was a random intercept
corresponding to ith PSU with u i ∼N 0,σ2 u and γðconfoundersÞ ij
representsthelineartermsforallconfoundersadjusted.
Further,theestimatesofPM componentsonanaemiaandARI
2.5
were stratified by birth weight status (LBW vs. Normal) using an
interaction component of LBW with the component in 2nd stage
model.Stratifiedanalysiswasperformedbyaddinganinteractionterm
tothestage-IImodel.Potentialnonlinearassociationsofpollutantson
healthoutcomeswereexploredbyreplacingP iwithapenalizedcubic
smoothingsplinefunctionofP i[i.e.,fðP
i
Þ]inthestage-IImodel.
To compare PM components specific estimate against the
2.5
effectsestimateforelevationofwholePM masswealsoestimated
2.5
adjustedORofPM massforLBW,anaemiaandARIbycluster-logistic
2.5
regression,clusterbeingthePSU.
Rversion4.1.2(RCoreTeam,2022Vienna,Austria)45wasusedfor
allstatisticalanalyses.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper describes a large cohort epidemiological quantification of the effects of PM2.5 components and their contributing sources on three neonatal/young child adverse health outcomes in India. The three outcomes are low birth weight, and anaemia and acute respiratory infection in under 5s. Individual (child and mother) and household-level data on health and potential modifiers and confounders are available for 152,000 to 177,000 instances (numbers differ according to health outcome). The exposures to total PM2.5 and to chemical components within the PM2.5 are estimated from a combination of satellite data and WRF-CMAQ chemical transport modelling. The PM2.5 contributions by source are estimated by modelling (using the ‘brute force’ approach of switching off sources in turn).\n\nThe paper deals with a topic of very substantial societal impact and policy implication. Exposure to PM2.5 is well known to cause the greatest mortality and morbidity of any environmental hazard globally. ‘Start of life’ is part of the life course with likely greater susceptibility to PM2.5 pollution than other parts. The extent of differential toxicity of different PM2.5 components and sources remains unresolved. The population of India is generally exposed to much greater PM2.5 concentrations than those in developed countries so there is clear need to quantify the burdens and to identify where mitigation is best focused. This study makes a substantial contribution to the knowledge base for these child health impacts in India.\n\nDescription of methods and results is well written and focused. The methods used appear appropriate. There are always limitations in estimating PM2.5 total and PM2.5 compositional exposure for these sorts of studies. The authors describe some of these limitations. Different methodological approaches will yield different estimates of PM2.5 exposure, and different splits of the chemical and source contributions to the exposure, but such differences are unlikely to alter the broad scientific findings.\n\nThe following are two areas where the authors should include additional explanatory discussion.\n(1) The substantial curvatures in the OR plots shown in Supplementary Figures S2-S4.\n(2) The realism or not of the very small contributions of elemental carbon (EC) to all sources, particularly to those where EC emissions are expected such as transport, power, and waste burning.\nL509: the word “interaction” is duplicated.", "reviewer_2": "This manuscript analyzed the health effect (acute respiratory infection and low birth rate) associated with PM exposure on children in India. The topic of health effect of PM2.5 is relevant to the scope of Nature Communications, but there are some major issues in the draft and the draft is hard for readers to follow. I would only recommend this manuscript to be accepted with major modification.\n\nMajor concerns\nLines 294 to 315, the description of model application is limited. Readers cannot find detail settings of the model like domain settings, resolution, performance evaluation etc. This made the simulation process like a black box.\n\nLines 165 to 167, I could not find how OR is calculated at Method section and Lines 343 to 358 is too general without any detailed information for how health data is used.", "reviewer_3": "I congratulate the authors in conducting this secondary analysis of data on exploring the estimation of impact of PM2.5 and its constituent components.\n\nI have few observations in your manuscript (not in any order of priority or importance), as follows:\n1. WHO estimates that 99% of the population are living in areas exposed to higher than 5Mcg/m3 of PM2.5. This is the current understanding. Please correct as you have mentioned 90%.\n2. Editorial comment. Need to improve the flow in write up such as the following should come early in the para and then focusing on children's health impact. \"The latest round of the state-level burden of diseases in India estimated that ambient PM2.5 exposure was responsible for 0.98 million (0.77-1.19) deaths and 17.8 million disability-adjusted life years.\"\n3. Why the transport sector has the lowest impact on LBW and higher for ARI? Are these random variation in data or some specific reason? These variations occur in the estimates need to have good discussion with references, if possible. \"Among the eight PM2.5 sources analyzed using the two-stage model, we found that the IQR increase in PM2.5 from road dust exhibited a higher effect on LBW (OR: 1.13, 1.11- 1.14), followed by the international transboundary transport (OR: 1.09, 1.07 – 1.1), the industry sector (OR: 1.07, 1.05 - 1.08), the agricultural sector (OR: 1.06, 1.05– 1.07), other sectors (OR: 1.04, 1.02 – 1.07) and transport sector (OR: 1.05, 1.02 –114 1.07).\"\n4. Exposure were average over pregnancy length (what about variable duration of pregnancy length) and average over child age since birth [Was it monthly data or daily data?]\n5. Now new DHS data is available as you are utilizing decade old data for estimating the health impact which may not be relevant in 2023.\n\nI would appreciate addressing or responding to these comments."}, "author_response": "Reviewer’s comments\nResponses to the reviewers’ comments are provided below in blue colour fonts.\nReviewer #1\nThis paper describes a large cohort epidemiological quantification of the effects of PM2.5 components and their contributing sources on three neonatal/young child adverse health outcomes in India. The three outcomes are low birth weight, and anaemia and acute respiratory infection in under 5s. Individual (child and mother) and household-level data on health and potential modifiers and confounders are available for 152,000 to 177,000 instances (numbers differ according to health outcome). The exposures to total PM2.5 and to chemical components within the PM2.5 are estimated from a combination of satellite data and WRF-CMAQ chemical transport modelling. The PM2.5 contributions by source are estimated by modelling (using the ‘brute force’ approach of switching off sources in turn).\n\nThe paper deals with a topic of very substantial societal impact and policy implication. Exposure to PM2.5 is well known to cause the greatest mortality and morbidity of any environmental hazard globally. ‘Start of life’ is part of the life course with likely greater susceptibility to PM2.5 pollution than other parts. The extent of differential toxicity of different PM2.5 components and sources remains unresolved. The population of India is generally exposed to much greater PM2.5 concentrations than those in developed countries so there is clear need to quantify the burdens and to identify where mitigation is best focused. This study makes a substantial contribution to the knowledge base for these child health impacts in India.\n\nDescription of methods and results is well written and focused. The methods used appear appropriate. There are always limitations in estimating PM2.5 total and PM2.5 compositional exposure for these sorts of studies. The authors describe some of these limitations. Different methodological approaches will yield different estimates of PM2.5 exposure, and different splits of the chemical and source contributions to the exposure, but such differences are unlikely to alter the broad scientific findings.\n\nWe thank the reviewer for appreciating our work and providing insightful comments. We have addressed them. The detailed responses are mentioned below.\n\nThe following are two areas where the authors should include additional explanatory discussion.\n1) The substantial curvatures in the OR plots shown in Supplementary Figures S2-S4.\nThe exposure-response relationship of components OC, NO -, and NH + exhibited a 3 4 monotonic increase up to a midrange concentration value, whereas the rest of the components, such as EC, soil, and others, showed an inverted U-shaped pattern for LBW (Supplementary Fig. 2). However, for anaemia (Supplementary Fig. 3), the majority of components, such as EC, NH +, NO -, OC, SO 2-, and others, showed a monotonic increase 4 3 4 in the probability of anaemia prevalence with an increase in pollutant exposure. In contrast, for soil, the trend line witnessed a fall. However, these nonparametric curves are very much data-driven, so it is challenging to provide a comprehensive explanation of the observed curvature in this study, particularly when estimating the exposure at the cluster level. Therefore, we emphasized on the effects estimate at a linear scale and reported this as an exploration. We have now added two lines, stating this limitation of the study (lines 239-242).\nLines 239-242: The exposure-response curves were observed to be mostly nonlinear and non-monotonic, unadjusted confounding could be one of the causes. The true causes of the nonlinearity could be difficult to explain from this study based on a cross-sectional national survey with a cluster level air pollution exposure.\n\n(2) The realism or not of the very small contributions of elemental carbon (EC) to all sources, particularly to those where EC emissions are expected such as transport, power, and waste burning.\nResponse: We thank the reviewer for this comment. It helped us to re-check the estimates, and we figured out a minor plotting error in the previous figure. We have now updated the supplementary Fig. 5 (also attached below). Even in the revised plot, the EC contributions are relatively lower than some of the other components of PM . This is because the plots depict 2.5 not just the primary emissions of PM but also include the contribution of secondary inorganic 2.5 particulates which are formed due to the interaction of gases like NOx, SOx, and NH4. The sectors like transport and power are one of the biggest emitters of gaseous pollutants, i.e., NOx and SOx, respectively. These gaseous pollutant result in the formation of secondary particulates in the form of nitrates and sulphates, causing a decrease in the relative contribution of primary particulates like EC in total ambient PM concentrations. Further, 2.5 another reason for the estimated lower EC contribution is the underlying uncertainty in the bottom-up emission inventory fed into the model. Several studies (e.g., Singh et al., 2021, Venkataraman et al., 2018; Verma et al., 2017) have also reached similar conclusions that modelled BC fraction (in total PM ) in India usually remains underestimated. We hope that 2.5 improvement in the BC emission monitoring technique leading to refinement in future emission updates will fix this issue. Currently, this is the best data we have, and we mentioned this in the Discussion (lines 254-257). We feel that the broad conclusion will not be altered due to this.\nLines 254-257: We note that the modelled mass fractions of individual species may have some uncertainties depending on the representativeness of emission inventory, which was also highlighted in the literature.33 Nonetheless, this will not alter the broad conclusion of the study that the cumulative impact of PM components is greater than the impact of PM mass on 2.5 2.5 child health in India.\n\nL509: the word “interaction” is duplicated.\nResponse: Corrected\n\nReviewer #2\nThis manuscript analyzed the health effect (acute respiratory infection and low birth rate) associated with PM exposure on children in India. The topic of health effect of PM2.5 is relevant to the scope of Nature Communications, but there are some major issues in the draft and the draft is hard for readers to follow. I would only recommend this manuscript to be accepted with major modification.\n\nWe thank the reviewer for providing insightful comments. We have addressed them. The detailed responses are mentioned below.\n\nMajor concerns\nLines 294 to 315, the description of model application is limited. Readers cannot find detail settings of the model like domain settings, resolution, performance evaluation etc. This made the simulation process like a black box.\n\nResponse:\nThe model speciation dataset used in the study is a published dataset taken from Singh et al., 2020. We realize and agree that some details would make the readers follow the discussion easily. We have now mentioned details about the domain setting, resolution, and performance evaluation in lines 303 to 313. Also, we have added a table (Supplementary Table 3; also attached below) in the supplementary to provide model setup configuration.\n\nLines 303 to 313 Satellite data cannot provide direct information on PM composition, and 2.5 very sparse ground measurements are available in India, that too for a very limited duration. Therefore, we integrated satellite-PM data from a published study which employed WRF 2.5 version 3.9.1-CMAQ version 5.3.1 modelling set up to simulate ambient PM concentrations 2.5 at 36 x 36 km2 spatial resolution with 25 vertical levels.33 The modelling set-up (Supplementary Table 3) employed ERA5 meteorology and emissions estimated using Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS)-ASIA model (https://gains.iiasa.ac.at) in which government reported energy consumption data for the different sectors was used as an input for the year 2016. ECLIPSE (version 5) database of IIASA (2014) had been used for the national ammonia emissions, ship emissions, and the emissions emitted from the neighbouring countries which fall within the study domain that, includes Bhutan, Nepal, Myanmar, Bangladesh, Sri Lanka, Pakistan, and parts of China and Afghanistan. Additionally, transboundary pollutants coming from outside the study domain were taken from boundary conditions developed by the Community Atmosphere Model with Chemistry (CAM-chem) model (https://www.acom.ucar.edu/cam-chem/cam-chem.shtml). The ambient PM 2.5 simulated concentrations showed a significant agreement for the coefficient of determination when compared with the observed concentrations at ground-based monitoring stations.33 The coefficient of determination between the observed versus simulated monthly averaged concentrations was found to be 0.81, while the index of agreement was 0.94.\n\nSupplementary Table 3: WRF-CMAQ Model set-up details\nData/scheme type Options\nModel resolution 36 x 36 km with 25 vertical levels\nMeteorology data used ECMWF’s ERA-5\nSimulation period The model was simulated from 1st Jan 2016 to 31st Dec 2016.\nMicro Physics Options in WRF WSM three-class simple ice scheme1\nShortwave radiation in WRF Dudhia Shortwave Scheme2\nLongwave radiation in WRF RRTM Longwave Scheme3\nSurface layer Revised MM5 Monin-Obukhov scheme4\nLand Surface Options in WRF Unified Noah Land Surface Model scheme5\nBoundary layer Options in WRF ACM26\nCumulus Options in WRF Kain-Fritsch (new Eta) scheme7\nChemical mechanism used in CMAQ CB6r3_ae7_aq (CB6r3 - Carbon Bond 6 version r3; ae7_aq module - CMAQ’s aero7 for treatment of SOA set up for standard cloud chemistry)8,9\nEmission inventory resolution 36x36 km\nEmissions of neighbouring countries within the ECLIPSE (version 5) database of IIASA study domain\nTransboundary pollutant outside the study Community Atmosphere Model with domain Chemistry (CAM-chem) model\n\nLines 165 to 167, I could not find how OR is calculated at Method section and Lines 343 to 358 is too general without any detailed information for how health data is used.\nResponse: We thank the reviewer for raising this issue. We have now added a few lines (167,173,180; corresponding to 165-167) that must clarify the concern. These ORs were stated in the previous subsection (lines 147-154). The method is also included in the ‘method’ section now (lines 393-395)\n\nLines 167-186:\nwhere (cid:1844)(cid:1844) =(cid:1857)(cid:1876)(cid:1868)( (cid:1864)(cid:1867)(cid:1859)( (cid:1841)(cid:1844))×∆(cid:1842)(cid:1839) ), and OR is the estimated odds ratio for each unit increase (cid:2870).(cid:2873) of PM exposure reported in previous subsection (ORs were converted for unit increase of 2.5 PM ). Finally, we calculated the expected reduction in district-level prevalence (E) for LBW, 2.5 anaemia, and ARI due to the reduction in PM exposure as:\n2.5\n(cid:1831) =(cid:1827)(cid:1832)×(cid:1830)(cid:1861)(cid:1871)(cid:1872)(cid:1870)(cid:1861)(cid:1855)(cid:1872) (cid:1864)(cid:1857)(cid:1874)(cid:1857)(cid:1864) (cid:1868)(cid:1870)(cid:1857)(cid:1874)(cid:1853)(cid:1864)(cid:1857)(cid:1866)(cid:1855)(cid:1857)\nWhile we consider OR for elevation of PM mass (i.e., 1.005,1.01 and 1.011 for LBW, 2.5 anaemia and ARI respectively) the overall LBW prevalence could reduce from 16.6% (16.4, 16.7) to 14.5% (14.5, 14.9) if the NAAQS level is achieved. If the exposure is reduced to the WHO-AQG level, the LBW prevalence could reduce to 11.6% (11.1, 12.4). For anaemia, the prevalence could reduce from 56.8% (56.6, 57.1) to 44.8% (43.8, 45.7) and further to 32.9% (32.1, 33.6), respectively, while for ARI, the prevalence could reduce from 2.8% (2.7, 2.9) to 2.1% (1.9, 2.3) and further to 1.5% (1.3, 1.6) if the NAAQS and WHO-AQG levels are achieved.\n\nWhile we consider cumulative OR for elevation of PM components (i.e., 1.021,1.041 and 2.5 1.03 for LBW, anaemia and ARI respectively, the overall LBW prevalence could reduce from 16.6% (16.4, 16.7) to 15.7% (15.3, 16.1) if the NAAQS level is achieved. If the exposure is reduced to the WHO-AQG level, the LBW prevalence could reduce to 14.7% (14.3, 15.09). For anaemia, the prevalence could reduce from 56.8% (56.6, 57.1) to 50.7% (49.7, 51.8) and further to 44.2% (43.3, 45.1), respectively, while for ARI, the prevalence could reduce from 2.8% (2.7, 2.9) to 2.3% (2.1, 2.7) and further to 2.1% (1.8, 2.5) if the NAAQS and WHO-AQG levels are achieved.\n\nLines 393-395: To compare PM components specific estimate against the effects estimate 2.5 for elevation of whole PM mass we also estimated adjusted OR of PM mass for LBW, 2.5 2.5 anaemia and ARI by cluster-logistic regression, cluster being the PSU.\n\nWe have now modified the entire subsection of covariate selection (Lines 353 to 369 corresponding to lines 343-358) which has now detailed specification of health outcome-specific covariates.\n\nLines 353-369:\nCovariates. Several individual-level and household-level variables were identified as potential covariates of LBW39,40, anaemia41, and ARI42. These variables were included in the analysis based on their significance with the respective health outcome. The following individual-level variables were considered for all three outcome variables: sex of the child (male or female), mother's education (no education, primary, secondary, and higher), and parity (1, or >1). Additionally, for the outcome variable ARI, we accounted for the age of the child, for LBW, mother’s age (<20,20-35,>35) and body mass index (BMI) of the mother (underweight, normal weight, overweight and obesity), lastly, for anaemia, maternal haemoglobin levels, and per capita iron intake. Daily dietary iron intake (per capita) was obtained by converting monthly food purchases captured by the 9th quinquennial Household Consumer Expenditure survey of the 68th round of the NSSO43 which was further triangulated with NFHS-4 by Swaminathan et al.,2019.44 Household-level covariates used for both ARI and anaemia were the following: socioeconomic status, which is classified into five wealth quintiles (poorest, poor, middle, rich, and richest), type of residence (rural or urban), and passive smoking (yes or no). Since studies have shown that ambient20 and household36 air pollution are associated with anaemia, we also included the type of cooking fuel in the household as a covariate. This was classified as clean fuel (electricity, LPG/natural gas, biogas), solid fuel (coal/lignite, charcoal, wood, straw/shrubs/grass, agricultural crop waste, and dung cakes), kerosene, and others. For LBW, only the wealth index was used as the household-level covariate.\n\nReviewer #3\nI congratulate the authors in conducting this secondary analysis of data on exploring the estimation of impact of PM2.5 and its constituent components.\n\nI have few observations in your manuscript (not in any order of priority or importance), as follows:\n\nWe thank the reviewer for appreciating our work and providing insightful comments. We have addressed them. The detailed responses are mentioned below.\n\n1. WHO estimates that 99% of the population are living in areas exposed to higher than 5Mcg/m3 of PM2.5. This is the current understanding. Please correct as you have mentioned 90%.\nResponse: Thanks for pointing this out. It was a typo error. Now we have replaced 90% to 99% (line 41).\n\n2. Editorial comment. Need to improve the flow in write up such as the following should come early in the para and then focusing on children's health impact. \"The latest round of the state-level burden of diseases in India estimated that ambient PM2.5 exposure was responsible for 0.98 million (0.77-1.19) deaths and 17.8 million disability-adjusted life years.\"\nResponse: Agree with the suggestion. Required modifications are implemented (lines 47,48).\n\n3. Why the transport sector has the lowest impact on LBW and higher for ARI? Are these random variation in data or some specific reason? These variations occur in the estimates need to have good discussion with references, if possible. \"Among the eight PM2.5 sources analyzed using the two-stage model, we found that the IQR increase in PM2.5 from road dust exhibited a higher effect on LBW (OR: 1.13, 1.11- 1.14), followed by the international transboundary transport (OR: 1.09, 1.07 – 1.1), the industry sector (OR: 1.07, 1.05 - 1.08), the agricultural sector (OR: 1.06, 1.05– 1.07), other sectors (OR: 1.04, 1.02 – 1.07) and transport sector (OR: 1.05, 1.02 –114 1.07).\"\nResponse: Proper explanation for this is difficult at this stage with a modelled data set. However, one possible reason could be the nature of the response of health to air pollution exposure. ARI is an acute response that could be higher for acute and highly variable PM 2.5 exposure sourced from the transport sector. If the reviewer permits, we would like to refrain from stating such an explanation at this stage, rather we want the reader to interpret this pattern as an observation that needs to be confirmed by a prospective cohort study later.\n\n4. Exposure were average over pregnancy length (what about variable duration of pregnancy length) and average over child age since birth [Was it monthly data or daily data?]\nResponse: We are sorry for the confusion. We have now modified the section such that the exposure assessment (lines 285-291) process gets clear to all. The average period was not the same for everyone. It was decided based on individual gestational age only. For LBW, the exposure was averaged over the pregnancy period using pregnancy duration (in months) and month of delivery, while for the ARI and anaemia, the exposure was averaged for the early-life duration from the birth month to the month of the survey.\nLines 285-291:\nAmbient PM exposure data. Our primary exposure metric was ambient PM exposure. Monthly 2.5 2.5 average PM concentrations at PSU level were used to construct long term exposure for three different 2.5 health outcomes. For low birth weight, the average of monthly PM ambient concentrations at the PSU 2.5 during the pregnancy with number of months being derived based on individual gestational age was considered as pregnancy period exposure. For anemia and ARI, the PM exposure was derived by 2.5 averaging monthly ambient PM concentration at the PSU over the period of individual life course. We therefore,\n\n5. Now new DHS data is available as you are utilizing decade old data for estimating the health impact which may not be relevant in 2023.\nResponse: It Is a great suggestion. However, due to exposure data (PM2.5 composition) constraints for NFHS-5, we considered NFHS-4 happened during 2015-2016, which temporally matches with CMAQ data (2016). The latest survey of DHS for India i.e., NFHS-5 happened during 2019-2021 and we have not yet developed the composition data for this period. Extrapolation of the same 2016 model data may end up with a biased estimation for NHFS-5. This can be a future study."}, {"round": 2, "reviewer_comments": {"reviewer_1": "This is a revised paper for which I was one of the original reviewers.\nI was supportive of the paper at its original submission (see original review). I asked the authors to respond to a couple of points, which they have done so adequately with inclusion of additional text.\nI am happy to recommend publication.", "reviewer_2": "My comments were fully stressed now. Now I can recommend the draft to be accepted."}, "author_response": "Reviewer’s comments\nReviewer #1 (Remarks to the Author):\nI was supportive of the paper at its original submission (see original review). I asked the authors to respond to a couple of points, which they have done so adequately with inclusion of additional text.\nI am happy to recommend publication.\n\nWe thank the reviewer for appreciating and recommending our work for publication.\n\nReviewer #2 (Remarks to the Author):\nMy comments were fully stressed now. Now I can recommend the draft to be accepted.\n\nWe thank the reviewer for recommending our work for publication."}]

Decision letter